Existing speech tokenizers typically assign a fixed number of tokens per second, regardless of the varying information density or temporal fluctuations in the speech signal. This uniform token allocation mismatches the intrinsic structure of speech, where information is distributed unevenly over time. To address this, we propose VARSTok, a VAriable-frame-Rate Speech Tokenizer that adapts token allocation based on local feature similarity. VARSTok introduces two key innovations: (1) a temporal-aware density peak clustering algorithm that adaptively segments speech into variable-length units, and (2) a novel implicit duration coding scheme that embeds both content and temporal span into a single token index, eliminating the need for auxiliary duration predictors. Extensive experiments show that VARSTok significantly outperforms strong fixed-rate baselines. Notably, it achieves superior reconstruction naturalness while using up to 23% fewer tokens than a 40 Hz fixed-frame-rate baseline. VARSTok further yields lower word error rates and improved naturalness in zero-shot text-to-speech synthesis. To the best of our knowledge, this is the first work to demonstrate that a fully dynamic, variable-frame-rate acoustic speech tokenizer can be seamlessly integrated into downstream speech language models.

We propose UniVocal, a unified framework that implicitly infers vocal modes from text context to pioneer Speech-Singing Code-Switching (SCS) Synthesis-a task where transitions are autonomously driven by textual semantics, akin to seamless human language blending.Unlike single-mode generation or systems relying on switching-control tags, our proposed UniVocal implicitly infers vocal modes solely from text context.To achieve this, we employ a data-efficient two-stage curriculum learning strategy that progressively trains a competitive TTS system to acquire the desired SCS capability.Addressing data scarcity, we introduce a scalable pipeline to synthesize diverse code-switching data that is both semantically and acoustically natural, alongside a new multi-scenario benchmark, SCSBench.To address limitations of semantic tokenizers in capturing acoustic details, we also introduce refined cent token and Chainof-Thought (CoT) generation for planning prosody before content generation, effectively enhancing empathetic speech generation and singing melody.Experimental results demonstrate that UniVocal achieves state-of-the-art performance on SCSBench while maintaining competitive performance on regular speech and singing tasks.

It was recently revealed that using features extracted from pre-trained models can achieve much better performance than using conventional hand-crafted acoustic features for spoofing speech detection. In this paper, we therefore enhance the features from pre-trained model based on recursive learning. Specifically, we modify the pre-trained model by feeding the features from the topmost transformer layer to bottom layers recursively, and the obtained recursive features from the bottom layers are fused with that from topmost layer. The fused features are then fed into the backend classifiers. Experiments are carried out on two benchmark datasets (i.e., ASVspoof 2019 LA and ASVspoof 2021 LA), which show the superiority of the proposed method over state-of-the-art systems.

Cross-lingual voice conversion (XVC) is a technology that modifies speaker identity while preserving linguistic content in scenarios where the source and target speakers use different languages. Previous non-parallel disentanglement-based methods face severe training-testing inconsistency issues in XVC tasks due to language mismatch and the lack of multilingual parallel data, which inevitably compromise the quality of the synthesized speech. In this paper, we propose CASC-XVC, a zero-shot XVC method incorporating with content accordant (CA) and speaker contrastive (SC) losses. Specifically, this method adopts the framework of FreeVC-s as the backbone. We design a cross-lingual fine-tuning process employing pairs of utterances from speakers in different languages to update the modules used in the inference stage. A CA loss and an SC loss are introduced to deal with the lack of true parallel targets in the fine-tuning process. Moreover, we use shared self-supervised learning (SSL) representations across different languages along with information perturbation for content disentanglement. Both subjective and objective results on a bilingual (English and Chinese) dataset demonstrate that our approach achieves significant improvements in XVC tasks.

In this paper, we propose a noise-invariant speaker representation learning (SRL) approach by aligning noisy-clean speech pairs at both the feature and embedding levels for model training. Specifically, we first construct noisy-clean pairs using data augmentation during training. The noisy features are then processed by a Conformer-based enhancement module. The feature-level alignment is achieved by minimizing the mean squared error between the enhanced and original clean data. At the embedding level, we introduce a supervised contrastive learning loss with noise-adaptive margin to simultaneously enhance the intra-speaker compactness and the inter-speaker separability and better adapt different noise levels, in combination with the Barlow Twins self-supervised loss to align the noisy-clean data pairs and reduce noise redundancy in the embedding space. Finally, these loss components are integrated with conventional classification loss to train the SRL network. Experimental results on various VoxCeleb1 test sets synthesized with noise sources demonstrate the effectiveness of the proposed method.

Recent advances in automatic speech recognition (ASR) technology have boosted the viability of fully automated Alzheimer’s disease (AD) detection via ASR transcripts. However, there is a lack of understanding of how ASR errors affect the performance of AD detection. This paper addresses that gap. First, we fine-tune 18 ASR models on three datasets from DementiaBank, generating 36 ASR transcripts on the ADReSS dataset (18 from original and 18 from fine-tuned ASR models). We then employ two AD detection methods using either ASR or manual transcripts: fine-tuning four large language models (LLMs) and fusing LLMs with pre-trained language models (PLMs). The results show that certain ASR transcripts outperform manual transcripts, suggesting that ASR errors provide valuable clues for AD detection. Finally, we conduct an interpretability study, including linguistic and SHapley Additive exPlanations (SHAP) analyses. This study reveals that greater word distribution differences between AD and healthy control (HC) groups in ASR transcripts may be linked to these valuable clues. This paper highlights the potential of ASR as a powerful tool for developing fully automated AD detection systems.

This paper proposes an Incremental Disentanglement-based Environment-Aware zero-shot text-to-speech (TTS) method, dubbed IDEA-TTS, that can synthesize speech for unseen speakers while preserving the acoustic characteristics of a given environment reference speech. IDEA-TTS adopts VITS as the TTS backbone. To effectively disentangle the environment, speaker, and text factors, we propose an incremental disentanglement process, where an environment estimator is designed to first decompose the environmental spectrogram into an environment mask and an enhanced spectrogram. The environment mask is then processed by an environment encoder to extract environment embeddings, while the enhanced spectrogram facilitates the subsequent disentanglement of the speaker and text factors with the condition of the speaker embeddings, which are extracted from the environmental speech using a pretrained environment-robust speaker encoder. Finally, both the speaker and environment embeddings are conditioned into the decoder for environment-aware speech generation. Experimental results demonstrate that IDEA-TTS achieves superior performance in the environment-aware TTS task, excelling in speech quality, speaker similarity, and environmental similarity. Additionally, IDEA-TTS is also capable of the acoustic environment conversion task and achieves state-of-the-art performance.

In this paper, a multi-scale feature fusion paradigm is proposed to fully exploit the power of the pre-trained models for text-independent speaker verification. It contains a front-end feature extractor and an enhanced ECAPA-TDNN backend in a cascade manner. The feature extractor incorporates local representations of the CNN layers as well as the global clues of the Transformer layers of the pre-trained models, which are combined to construct the multi-scale discriminative features. The outputs of the feature extractor are then fed into the back-end model (tailored from ECAPA-TDNN) to obtain the final speaker embedding. Results on VoxCeleb datasets validate the superiority of the proposed method with equal error rates of 0.633% and 0.457% on the official trials of Vox1-O using the base and large pre-trained models, respectively.

Recently, mainstream mel-spectrogram-based neural vocoders rely on generative adversarial network (GAN) for high-fidelity speech generation, e.g., HiFi-GAN and BigVGAN. However, the use of GAN restricts training efficiency and model complexity. Therefore, this letter proposes a novel FreeGAN vocoder, aiming to answer the question of whether GAN is necessary for mel-spectrogram-based neural vocoders. The FreeGAN employs an amplitude-phase serial prediction framework, eliminating the need for GAN training. It incorporates amplitude prior input, SNAKE-ConvNeXt v2 backbone and frequency-weighted anti-wrapping phase loss to compensate for the performance loss caused by the absence of GAN. Experimental results confirm that the speech quality of FreeGAN is comparable to that of advanced GAN-based vocoders, while significantly improving training efficiency and complexity. Other explicit-phase-prediction-based neural vocoders can also work without GAN, leveraging our proposed methods.

This paper proposes StreamCodec, a streamable neural audio codec designed for real-time communication. StreamCodec adopts a fully causal, symmetric encoder-decoder structure and operates in the modified discrete cosine transform (MDCT) domain, aiming for low-latency inference and real-time efficient generation. To improve codebook utilization efficiency and compensate for the audio quality loss caused by structural causality, StreamCodec introduces a novel residual scalar-vector quantizer (RSVQ). The RSVQ sequentially connects scalar quantizers and improved vector quantizers in a residual manner, constructing coarse audio contours and refining acoustic details, respectively. Experimental results confirm that the proposed StreamCodec achieves decoded audio quality comparable to advanced non-streamable neural audio codecs. Specifically, on the 16 kHz LibriTTS dataset, StreamCodec attains a ViSQOL score of 4.30 at 1.5 kbps. It has a fixed latency of only 20 ms and achieves a generation speed nearly 20 times real-time on a CPU, with a lightweight model size of just 7 M parameters, making it highly suitable for real-time communication applications.

Low-bitrate speech coding plays an essential role in speech transmission and storage. However, speech quality degrades noticeably at low bitrates with current coding methods. Therefore, this letter proposes a novel Token-Prediction-based Post-Processing (T3P) model to improve the quality of low-bitrate coded speech. Unlike existing post-processing methods, T3P is a discrete-domain method centered on the prediction and classification of discrete tokens. Specifically, given low-bitrate coded speech features as condition, T3P initiates from a random token and sequentially predicts the token sequences produced by a residual vector quantization (RVQ) based neural codec, which is subsequently decoded to reconstruct the raw speech. Experiments confirm that T3P surpasses flow-matching-based and speech-enhancement-based baselines, achieving a better trade-off between speech quality and efficiency. Empowered by T3P, Encodec achieves performance at just 0.5 kbps that exceeds its original 4 kbps results for 16 kHz speech coding.

Recently, fine-grained prosody representations have emerged and attracted growing attention to address the one-to-many problem in text-to-speech (TTS). In this article, we propose the PhonemeVec, a pre-trained prosody representations with considering the contextual information. To obtain the contextual prosody representations, we improve the data2vec framework according to the characteristics of prosody to extract the PhonemeVec from the low-band mel-spectrogram, and pre-train on a 960 hours Chinese corpus with high quality and diverse pronunciation. PhonemeVec is subsequently integrated into FastSpeech2, supervising the prosody modeling of the text encoder. Experiments conducted on the Blizzard Challenge 2019 dataset show that the integration of PhonemeVec results in the synthesis of more natural speech. Additionally, objective evaluations confirm that the application of PhonemeVec reduces the distortions between the generated speech and original recordings in terms of duration and F0. Audio samples can be found at http://home.ustc.edu.cn/~wsmzzz/PhonemeVec/demo.html .

Accurate preoperative recurrence prediction for non-small cell lung cancer (NSCLC) is a challenging issue in the medical field. Existing studies primarily conduct image and molecular analyses independently or directly fuse multimodal information through radiomics and genomics, which fail to fully exploit and effectively utilize the highly heterogeneous cross-modal information at different levels and model the complex relationships between modalities, resulting in poor fusion performance and becoming the bottleneck of precise recurrence prediction. To address these limitations, we propose a novel unified framework, the Self-and-Mutual Attention (SAMA) Network, designed to efficiently fuse and utilize macroscopic CT images and microscopic gene data for precise NSCLC recurrence prediction, integrating handcrafted features, deep features, and gene features. Specifically, we design a Self-and-Mutual Attention Module that performs three-stage fusion: the self-enhancement stage enhances modality-specific features; the gene-guided and CT-guided cross-modality fusion stages perform bidirectional cross-guidance on the self-enhanced features, complementing and refining each modality, enhancing heterogeneous feature expression; and the optimized feature aggregation stage ensures the refined interactive features for precise prediction. Extensive experiments on both publicly available datasets from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) demonstrate that our method achieves state-of-the-art performance and exhibits broad applicability to various cancers.

Conversational speech synthesis aims to synthesize speech of an individual speaker based on history conversation. However, most studies in conversational speech synthesis only focus on the synthesis performance of the current speaker’s turn and neglect the temporal relationship between turns of interlocutors. Therefore, we consider the temporal connection between turns for conversational speech synthesis, which is crucial for the naturalness and coherence of conversations. Specifically, this paper formulates a task in which there is no overlap between turns and only one history turn is considered. To complete this task, an acoustic model is proposed which leverages multi-modal (including text and speech) information from previous turn to predict the acoustic features of not only current turn but also the inter-turn gap. The model is designed based on MQTTS and incorporates the global acoustic representation and BERT-based local semantic representation of previous turn when predicting the acoustic features of each frame. Experimental results demonstrate that with the introduction of global acoustic information and local semantic information, our model achieves better performance on the temporal connection between turns and the quality of synthetic speech. Audio samples can be found in https://mkd-mkd.github.io/icassp2024.

This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing real speech. This task falls under the umbrella of articulatory-to-acoustic (A2A) conversion and may also be referred to as a silent speech interface. To overcome the domain discrepancy between silent and standard vocalized articulation, we introduce a novel pseudo target generation strategy. It integrates the text modality to align with articulatory movements, thereby guiding the generation of pseudo acoustic features for supervised training on speech reconstruction from silent articulation. Furthermore, we propose to employ a denoising diffusion probabilistic model as the fundamental architecture for the A2A conversion task and train the model using a combined training approach with the generated pseudo acoustic features. Experiments show that our proposed method significantly improves the intelligibility and naturalness of the reconstructed speech in the silent speaking mode compared to all baseline methods. Specifically, the word error rate of the reconstructed speech decreases by approximately 5% when measured using an automatic speech recognition engine for intelligibility assessment, and the subjective mean opinion score for naturalness improves by 0.14. Moreover, analytical experiments reveal that the proposed pseudo target generation strategy can generate pseudo acoustic features that synchronize better with articulatory movements than previous strategies. Samples are available at our project page.

In this paper, we propose MDCTCodec, an efficient lightweight end-to-end neural audio codec based on the modified discrete cosine transform (MDCT). The encoder takes the MDCT spectrum of audio as input, encoding it into a continuous latent code which is then discretized by a residual vector quantizer (RVQ). Subsequently, the decoder decodes the MDCT spectrum from the quantized latent code and reconstructs audio via inverse MDCT. During the training phase, a novel multi-resolution MDCT-based discriminator (MR-MDCTD) is adopted to discriminate the natural or decoded MDCT spectrum for adversarial training. Experimental results confirm that, in scenarios with high sampling rates and low bitrates, the MDCTCodec exhibited high decoded audio quality, improved training and generation efficiency, and compact model size compared to baseline codecs. Specifically, the MDCTCodec achieved a ViSQOL score of 4.18 at a sampling rate of 48 kHz and a bitrate of 6 kbps on the public VCTK corpus.

This paper proposes a novel Stage-wise and Prior-aware Neural Speech Phase Prediction (SP-NSPP) model, which predicts the phase spectrum from input amplitude spectrum by two-stage neural networks. In the initial prior-construction stage, we preliminarily predict a rough prior phase spectrum from the amplitude spectrum. The subsequent refinement stage transforms the amplitude spectrum into a refined high-quality phase spectrum conditioned on the prior phase. Networks in both stages use ConvNeXt v2 blocks as the backbone and adopt adversarial training by innovatively introducing a phase spectrum discriminator (PSD). To further improve the continuity of the refined phase, we also incorporate a time-frequency integrated difference (TFID) loss in the refinement stage. Experimental results confirm that, compared to neural network-based no-prior phase prediction methods, the proposed SP-NSPP achieves higher phase prediction accuracy, thanks to introducing the coarse phase priors and diverse training criteria. Compared to iterative phase estimation algorithms, our proposed SP-NSPP does not require multiple rounds of staged iterations, resulting in higher generation efficiency.

We participated in track 2 of the VoiceMOS Challenge 2024, which aimed to predict the mean opinion score (MOS) of singing samples. Our submission secured the first place among all participating teams, excluding the official baseline. In this paper, we further improve our submission and propose a novel Pitch-and-Spectrum-aware Singing Quality Assessment (PS-SQA) method. The PS-SQA is designed based on the self-supervised-learning (SSL) MOS predictor, incorporating singing pitch and spectral information, which are extracted using pitch histogram and non-quantized neural codec, respectively. Additionally, the PS-SQA introduces a bias correction strategy to address prediction biases caused by low-resource training samples, and employs model fusion technology to further enhance prediction accuracy. Experimental results confirm that our proposed PS-SQA significantly outperforms all competing systems across all system-level metrics, confirming its strong sing quality assessment capabilities.

This paper introduces a novel neural audio codec targeting high waveform sampling rates and low bitrates named APCodec, which seamlessly integrates the strengths of parametric codecs and waveform codecs. The APCodec revolutionizes the process of audio encoding and decoding by concurrently handling the amplitude and phase spectra as audio parametric characteristics like parametric codecs. It is composed of an encoder and a decoder with the modified ConvNeXt v2 network as the backbone, connected by a quantizer based on the residual vector quantization (RVQ) mechanism. The encoder compresses the audio amplitude and phase spectra in parallel, amalgamating them into a continuous latent code at a reduced temporal resolution. This code is subsequently quantized by the quantizer. Ultimately, the decoder reconstructs the audio amplitude and phase spectra in parallel, and the decoded waveform is obtained by inverse short-time Fourier transform. To ensure the fidelity of decoded audio like waveform codecs, spectral-level loss, quantization loss, and generative adversarial network (GAN) based loss are collectively employed for training the APCodec. To support low-latency streamable inference, we employ feed-forward layers and causal deconvolutional layers in APCodec, incorporating a knowledge distillation training strategy to enhance the quality of decoded audio. Experimental results confirm that our proposed APCodec can encode 48 kHz audio at bitrate of just 6 kbps, with no significant degradation in the quality of the decoded audio. At the same bitrate, our proposed APCodec also demonstrates superior decoded audio quality and faster generation speed compared to well-known codecs, such as Encodec, AudioDec and DAC.

This paper presents a novel neural speech phase prediction model which predicts wrapped phase spectra directly from amplitude spectra. The proposed model is a cascade of a residual convolutional network and a parallel estimation architecture. The parallel estimation architecture is a core module for direct wrapped phase prediction. This architecture consists of two parallel linear convolutional layers and a phase calculation formula, imitating the process of calculating the phase spectra from the real and imaginary parts of complex spectra and strictly restricting the predicted phase values to the principal value interval. To avoid the error expansion issue caused by phase wrapping, we design anti-wrapping training losses defined between the predicted wrapped phase spectra and natural ones by activating the instantaneous phase error, group delay error and instantaneous angular frequency error using an anti-wrapping function. We mathematically demonstrate that the anti-wrapping function should possess three properties, namely parity, periodicity and monotonicity. We also achieve low-latency streamable phase prediction by combining causal convolutions and knowledge distillation training strategies. For both analysis-synthesis and specific speech generation tasks, experimental results show that our proposed neural speech phase prediction model outperforms the iterative phase estimation algorithms and neural network-based phase prediction methods in terms of phase prediction precision, efficiency and robustness. Compared with HiFi-GAN-based waveform reconstruction method, our proposed model also shows outstanding efficiency advantages while ensuring the quality of synthesized speech.

Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement. This paper proposes the incorporation of ultrasound tongue images to improve the performance of lip-based AV-SE systems further. To address the challenge of acquiring ultrasound tongue images during inference, we first propose to employ knowledge distillation during training to investigate the feasibility of leveraging tongue-related information without directly inputting ultrasound tongue images. Specifically, we guide an audio-lip speech enhancement student model to learn from a pre-trained audio-lip-tongue speech enhancement teacher model, thus transferring tongue-related knowledge. To better model the alignment between the lip and tongue modalities, we further propose the introduction of a lip-tongue key-value memory network into the AV-SE model. This network enables the retrieval of tongue features based on readily available lip features, thereby assisting the subsequent speech enhancement task. Experimental results demonstrate that both methods significantly improve the quality and intelligibility of the enhanced speech compared to traditional lip-based AV-SE baselines. Moreover, both proposed methods exhibit strong generalization performance on unseen speakers and in the presence of unseen noises. Furthermore, phone error rate (PER) analysis of automatic speech recognition (ASR) reveals that while all phonemes benefit from introducing ultrasound tongue images, palatal and velar consonants benefit most.

This paper presents a novel speech phase prediction model which predicts wrapped phase spectra directly from amplitude spectra by neural networks. The proposed model is a cascade of a residual convolutional network and a parallel estimation architecture. The parallel estimation architecture is composed of two parallel linear convolutional layers and a phase calculation formula, imitating the process of calculating the phase spectra from the real and imaginary parts of complex spectra and strictly restricting the predicted phase values to the principal value interval. To avoid the error expansion issue caused by phase wrapping, we design anti-wrapping training losses defined between the predicted wrapped phase spectra and natural ones by activating the instantaneous phase error, group delay error and instantaneous angular frequency error using an anti-wrapping function. Experimental results show that our proposed neural speech phase prediction model outperforms the iterative Griffin-Lim algorithm and other neural network-based method, in terms of both reconstructed speech quality and generation speed.

Lip-to-Speech (Lip2Speech) synthesis, which predicts corresponding speech from talking face images, has witnessed significant progress with various models and training strategies in a series of independent studies. However, existing studies can not achieve voice control under zero-shot condition, because extra speaker embeddings need to be extracted from natural reference speech and are unavailable when only the silent video of an unseen speaker is given. In this paper, we propose a zero-shot personalized Lip2Speech synthesis method, in which face images control speaker identities. A variational autoencoder is adopted to disentangle the speaker identity and linguistic content representations, which enables speaker embeddings to control the voice characteristics of synthetic speech for unseen speakers. Furthermore, we propose associated cross-modal representation learning to promote the ability of face-based speaker embeddings (FSE) on voice control. Extensive experiments verify the effectiveness of the proposed method whose synthetic utterances are more natural and matching with the personality of input video than the compared methods. To our best knowledge, this paper makes the first attempt on zero-shot personalized Lip2Speech synthesis with a face image rather than reference audio to control voice characteristics.

This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing sound. This task falls under the umbrella of articulatory-to-acoustic conversion, and may also be refered to as a silent speech interface. We propose to employ a method built on pseudo target generation and domain adversarial training with an iterative training strategy to improve the intelligibility and naturalness of the speech recovered from silent tongue and lip articulation. Experiments show that our proposed method significantly improves the intelligibility and naturalness of the reconstructed speech in silent speaking mode compared to the baseline TaLNet model. When using an automatic speech recognition (ASR) model to measure intelligibility, the word error rate (WER) of our proposed method decreases by over 15% compared to the baseline. In addition, our proposed method also outperforms the baseline on the intelligibility of the speech reconstructed in vocalized articulating mode, reducing the WER by approximately 10%.

This paper proposes MP-SENet, a novel Speech Enhancement Network which directly denoises Magnitude and Phase spectra in parallel.The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by convolution-augmented transformers.The encoder aims to encode time-frequency representations from the input noisy magnitude and phase spectra.The decoder is composed of parallel magnitude mask decoder and phase decoder, directly recovering clean magnitude spectra and clean-wrapped phase spectra by incorporating learnable sigmoid activation and parallel phase estimation architecture, respectively.Multi-level losses defined on magnitude spectra, phase spectra, short-time complex spectra, and time-domain waveforms are used to train the MP-SENet model jointly.Experimental results show that our proposed MP-SENet achieves a PESQ of 3.50 on the public VoiceBank+DEMAND dataset and outperforms existing advanced speech enhancement methods.

Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement.This paper proposes further incorporating ultrasound tongue images to improve lip-based AV-SE systems' performance.Knowledge distillation is employed at the training stage to address the challenge of acquiring ultrasound tongue images during inference, enabling an audio-lip speech enhancement student model to learn from a pre-trained audiolip-tongue speech enhancement teacher model.Experimental results demonstrate significant improvements in the quality and intelligibility of the speech enhanced by the proposed method compared to the traditional audio-lip speech enhancement baselines.Further analysis using phone error rates (PER) of automatic speech recognition (ASR) shows that palatal and velar consonants benefit most from the introduction of ultrasound tongue images.

This paper presents a novel task, zero-shot voice conversion based on face images (zero-shot FaceVC), which aims at converting the voice characteristics of an utterance from any source speaker to a newly coming target speaker, solely relying on a single face image of the target speaker. To address this task, we propose a face-voice memory-based zero-shot FaceVC method. This method leverages a memory-based face-voice alignment module, in which slots act as the bridge to align these two modalities, allowing for the capture of voice characteristics from face images. A mixed supervision strategy is also introduced to mitigate the long-standing issue of the inconsistency between training and inference phases for voice conversion tasks. To obtain speaker-independent content-related representations, we transfer the knowledge from a pretrained zero-shot voice conversion model to our zero-shot FaceVC model. Considering the differences between FaceVC and traditional voice conversion tasks, systematic subjective and objective metrics are designed to thoroughly evaluate the homogeneity, diversity and consistency of voice characteristics controlled by face images. Through extensive experiments, we demonstrate the superiority of our proposed method on the zero-shot FaceVC task. Samples are presented on our demo website.

Speech phase prediction, which is a significant research focus in the field of signal processing, aims to recover speech phase spectra from amplitude-related features. However, existing speech phase prediction methods are constrained to recovering phase spectra with short frame shifts, which are considerably smaller than the theoretical upper bound required for exact waveform reconstruction of short-time Fourier transform (STFT). To tackle this issue, we present a novel long-frame-shift neural speech phase prediction (LFS-NSPP) method which enables precise prediction of long-frame-shift phase spectra from long-frame-shift log amplitude spectra. The proposed method consists of three stages: interpolation, prediction and decimation. The short-frame-shift log amplitude spectra are first constructed from long-frame-shift ones through frequency-by-frequency interpolation to enhance the spectral continuity, and then employed to predict short-frame-shift phase spectra using an NSPP model, thereby compensating for interpolation errors. Ultimately, the long-frame-shift phase spectra are obtained from short-frame-shift ones through frame-by-frame decimation. Experimental results show that the proposed LFS-NSPP method can yield superior quality in predicting long-frame-shift phase spectra than the original NSPP model and other signal-processing-based phase estimation algorithms.

This paper presents a novel neural vocoder named APNet which reconstructs speech waveforms from acoustic features by predicting amplitude and phase spectra directly. The APNet vocoder is composed of an amplitude spectrum predictor (ASP) and a phase spectrum predictor (PSP). The ASP is a residual convolution network which predicts frame-level log amplitude spectra from acoustic features. The PSP also adopts a residual convolution network using acoustic features as input, then passes the output of this network through two parallel linear convolution layers respectively, and finally integrates into a phase calculation formula to estimate frame-level phase spectra. Finally, the outputs of ASP and PSP are combined to reconstruct speech waveforms by inverse short-time Fourier transform (ISTFT). All operations of the ASP and PSP are performed at the frame level. We train the ASP and PSP jointly and define multilevel loss functions based on amplitude mean square error, phase anti-wrapping error, short-time spectral inconsistency error and time domain reconstruction error. Experimental results show that our proposed APNet vocoder achieves an approximately 8x faster inference speed than HiFi-GAN v1 on a CPU due to the all-frame-level operations, while its synthesized speech quality is comparable to HiFi-GAN v1. The synthesized speech quality of the APNet vocoder is also better than that of several equally efficient models. Ablation experiments also confirm that the proposed parallel phase estimation architecture is essential to phase modeling and the proposed loss functions are helpful for improving the synthesized speech quality.

This paper presents a denoising and dereverberation hierarchical neural vocoder (DNR-HiNet) to convert noisy and reverberant acoustic features into clean speech waveforms. The DNR-HiNet vocoder is built by modifying the amplitude spectrum predictor (ASP) in the original HiNet vocoder. This modified denoising and dereverberation ASP (DNR-ASP) can predict clean log amplitude spectra from input degraded acoustic features. To achieve this, the DNR-ASP first predicts the log amplitude spectra of noisy and reverberant speech, the log amplitude spectra of additive noise and the room impulse response (RIR) and then performs initial denoising and dereverberation by signal processing algorithms. The initially processed log amplitude spectra are then enhanced by another neural network to obtain the final clean log amplitude spectra. We also introduce a bandwidth extension model and a frequency resolution extension model into the DNR-ASP to further improve its performance. Finally, a statistical parametric speech synthesis (SPSS) method with DNR-HiNet is proposed to deal with the situation that the quality of target speaker’s recordings is degraded by noise and reverberation. Experimental results indicate that the DNR-HiNet vocoder was able to generate denoised and dereverberated waveforms given noisy and reverberant acoustic features and outperformed the original HiNet vocoder and a few other neural vocoders. On speech enhancement tasks, its performance was competitive with several advanced speech enhancement methods. Furthermore, the SPSS method with DNR-HiNet achieved better quality of synthetic speech than the conventional approach which directly applied speech enhancement to the degraded adaptation data.

This paper presents a denoising and dereverberation hierarchical neural vocoder (DNR-HiNet) to convert noisy and reverberant acoustic features into a clean speech waveform. We implement it mainly by modifying the amplitude spectrum predictor (ASP) in the original HiNet vocoder. This modified denoising and dereverberation ASP (DNR-ASP) can predict clean log amplitude spectra (LAS) from input degraded acoustic features. To achieve this, the DNR-ASP first predicts the noisy and reverberant LAS, noise LAS related to the noise information, and room impulse response related to the reverberation information then performs initial denoising and dereverberation. The initial processed LAS are then enhanced by another neural network as the final clean LAS. To further improve the quality of the generated clean LAS, we also introduce a bandwidth extension model and frequency resolution extension model in the DNR-ASP. The experimental results indicate that the DNR-HiNet vocoder was able to generate a denoised and dereverberated waveform given noisy and reverberant acoustic features and outperformed the original HiNet vocoder and a few other neural vocoders. We also applied the DNR-HiNet vocoder to speech enhancement tasks, and its performance was competitive with several advanced speech enhancement methods.

High-quality speech corpora are essential foundations for most speech applications. However, such speech data are expensive and limited since they are collected in professional recording environments. In this work, we propose an encoder-decoder neural network to automatically enhance low-quality recordings to professional high-quality recordings. To address channel variability, we first filter out the channel characteristics from the original input audio using the encoder network with adversarial training. Next, we disentangle the channel factor from a reference audio. Conditioned on this factor, an auto-regressive decoder is then used to predict the target-environment Mel spectrogram. Finally, we apply a neural vocoder to synthesize the speech waveform. Experimental results show that the proposed system can generate a professional high-quality speech waveform when setting high-quality audio as the reference. It also improves speech enhancement performance compared with several state-of-the-art baseline systems.

In our previous work, we have proposed a neural vocoder called HiNet which recovers speech waveforms by predicting amplitude and phase spectra hierarchically from input acoustic features. In HiNet, the amplitude spectrum predictor (ASP) predicts log amplitude spectra (LAS) from input acoustic features. This paper proposes a novel knowledge-and-data-driven ASP (KDD-ASP) to improve the conventional one. First, acoustic features (i.e., F0 and mel-cepstra) pass through a knowledge-driven LAS recovery module to obtain approximate LAS (ALAS). This module is designed based on the combination of STFT and source-filter theory, in which the source part and the filter part are designed based on input F0 and mel-cepstra, respectively. Then, the recovered ALAS are processed by a data-driven LAS refinement module which consists of multiple trainable convolutional layers to get the final LAS. Experimental results show that the HiNet vocoder using KDD-ASP can achieve higher quality of synthetic speech than that using conventional ASP and the WaveRNN vocoder on a text-to-speech (TTS) task.

This paper presents a reverberation module for source-filter-based neural vocoders that improves the performance of reverberant effect modeling. This module uses the output waveform of neural vocoders as an input and produces a reverberant waveform by convolving the input with a room impulse response (RIR). We propose two approaches to parameterizing and estimating the RIR. The first approach assumes a global time-invariant (GTI) RIR and directly learns the values of the RIR on a training dataset. The second approach assumes an utterance-level time-variant (UTV) RIR, which is invariant within one utterance but varies across utterances, and uses another neural network to predict the RIR values. We add the proposed reverberation module to the phase spectrum predictor (PSP) of a HiNet vocoder and jointly train the model. Experimental results demonstrate that the proposed module was helpful for modeling the reverberation effect and improving the perceived quality of generated reverberant speech. The UTV-RIR was shown to be more robust than the GTI-RIR to unknown reverberation conditions and achieved a perceptually better reverberation effect.

This article presents a neural vocoder named HiNet which reconstructs speech waveforms from acoustic features by predicting amplitude and phase spectra hierarchically. Different from existing neural vocoders such as WaveNet, SampleRNN and WaveRNN which directly generate waveform samples using single neural networks, the HiNet vocoder is composed of an amplitude spectrum predictor (ASP) and a phase spectrum predictor (PSP). The ASP is a simple DNN model which predicts log amplitude spectra (LAS) from acoustic features. The predicted LAS are sent into the PSP for phase recovery. Considering the issue of phase warping and the difficulty of phase modeling, the PSP is constructed by concatenating a neural source-filter (NSF) waveform generator with a phase extractor. We also introduce generative adversarial networks (GANs) into both ASP and PSP. Finally, the outputs of ASP and PSP are combined to reconstruct speech waveforms by short-time Fourier synthesis. Since there are no autoregressive structures in both predictors, the HiNet vocoder can generate speech waveforms with high efficiency. Objective and subjective experimental results show that our proposed HiNet vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder, a 16-bit WaveNet vocoder using open source implementation and an NSF vocoder with similar complexity to the PSP and obtains similar performance with a 16-bit WaveRNN vocoder. We also find that the performance of HiNet is insensitive to the complexity of the neural waveform generator in PSP to some extend. After simplifying its model structure, the time consumed for generating 1 s waveforms of 16 kHz speech using a GPU can be further reduced from 0.34 s to 0.19 s without significant quality degradation.

This paper presents a spectral enhancement method to improve the quality of speech reconstructed by neural waveform generators with low-bit quantization. At training stage, this method builds a multiple-target DNN, which predicts log amplitude spectra of natural high-bit waveforms together with the amplitude ratios between natural and distorted spectra. Log amplitude spectra of the waveforms reconstructed by low-bit neural waveform generators are adopted as model input. At generation stage, the enhanced amplitude spectra are obtained by an ensemble decoding strategy, and are further combined with the phase spectra of low-bit waveforms to produce the final waveforms by inverse STFT. In our experiments on WaveRNN vocoders, an 8-bit WaveRNN with spectral enhancement outperforms a 16-bit counterpart with the same model complexity in terms of the quality of reconstructed waveforms. Besides, the proposed spectral enhancement method can also help an 8-bit WaveRNN with reduced model complexity to achieve similar subjective performance with a conventional 16-bit WaveRNN.

This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS).Singing voice differs from speech and it contains more local dynamic movements of acoustic features, e.g., vibratos.Therefore, our method adopts deep autoregressive (DAR) models to predict the F0 and spectral features of singing voice in order to better describe the dependencies among the acoustic features of consecutive frames.For F0 modeling, discretized F0 values are used and the influences of the history length in DAR are analyzed by experiments.An F0 post-processing strategy is also designed to alleviate the inconsistency between the predicted F0 contours and the F0 values determined by music notes.Furthermore, we extend the DAR model to deal with continuous spectral features, and a prenet module with self-attention layers is introduced to process historical frames.Experiments on a Chinese singing voice corpus demonstrate that our method using DARs can produce F0 contours with vibratos effectively, and can achieve better objective and subjective performance than the conventional method using recurrent neural networks (RNNs).

This paper presents a SampleRNN-based neural vocoder for statistical parametric speech synthesis. This method utilizes a conditional SampleRNN model composed of a hierarchical structure of GRU layers and feed-forward layers to capture long-span dependencies between acoustic features and waveform sequences. Compared with conventional vocoders based on the source-filter model, our proposed vocoder is trained without assumptions derived from the prior knowledge of speech production and is able to provide a better modeling and recovery of phase information. Objective and subjective evaluations are conducted on two corpora. Experimental results suggested that our proposed vocoder can achieve higher quality of synthetic speech than the STRAIGHT vocoder and a WaveNet-based neural vocoder with similar run-time efficiency, no matter natural or predicted acoustic features are used as inputs.

This paper presents a waveform modeling and generation method using hierarchical recurrent neural networks (HRNN) for speech bandwidth extension (BWE). Different from conventional BWE methods that predict spectral parameters for reconstructing wideband speech waveforms, this BWE method models and predicts waveform samples directly without using vocoders. Inspired by SampleRNN, which is an unconditional neural audio generator, the HRNN model represents the distribution of each wideband or high-frequency waveform sample conditioned on the input narrowband waveform samples using a neural network composed of long short-term memory (LSTM) layers and feed-forward layers. The LSTM layers form a hierarchical structure and each layer operates at a specific temporal resolution to efficiently capture long-span dependencies between temporal sequences. Furthermore, additional conditions, such as the bottleneck features derived from narrowband speech using a deep neural network based state classifier, are employed as auxiliary input to further improve the quality of generated wideband speech. The experimental results of comparing several waveform modeling methods show that the HRNN-based method can achieve better speech quality and run-time efficiency than the dilated convolutional neural network based method and the plain sample-level recurrent neural network based method. Our proposed method also outperforms the conventional vocoder-based BWE method using LSTM-RNNs in terms of the subjective quality of the reconstructed wideband speech.