Recent advancements in audio language models have underscored the pivotal role of audio tokenization, which converts audio signals into discrete tokens, thereby facilitating the application of language model architectures to the audio domain. In this study, we introduce ALMTokenizer, a novel low-bitrate and semantically rich audio codec tokenizer for audio language models. Prior methods, such as Encodec, typically encode individual audio frames into discrete tokens without considering the use of context information across frames. Unlike these methods, we introduce a novel query-based compression strategy to capture holistic information with a set of learnable query tokens by explicitly modeling the context information across frames. This design not only enables the codec model to capture more semantic information but also encodes the audio signal with fewer token sequences. Additionally, to enhance the semantic information in audio codec models, we introduce the following: (1) A masked autoencoder (MAE) loss, (2) Vector quantization based on semantic priors, and (3) An autoregressive (AR) prediction loss. As a result, ALMTokenizer achieves competitive reconstruction performance relative to state-of-the-art approaches while operating at a lower bitrate. Within the same audio language model framework, ALMTokenizer outperforms previous tokenizers in audio understanding and generation tasks.[https://dongchaoyang.top/ALMTokenizer/]

Recent advances in text-to-speech synthesis have achieved notable success in generating high-quality short utterances for individual speakers. However, these systems still face challenges when extending their capabilities to long, multi-speaker, and spontaneous dialogues, typical of real-world scenarios such as podcasts. These limitations arise from two primary challenges: 1) long speech: podcasts typically span several minutes, exceeding the upper limit of most existing work; 2) spontaneity: podcasts are marked by their spontaneous, oral nature, which sharply contrasts with formal, written contexts; existing works often fall short in capturing this spontaneity. In this paper, we propose MoonCast, a solution for high-quality zero-shot podcast generation, aiming to synthesize spontaneous podcast-style speech from text-only sources (e.g., stories, technical reports, news in TXT, PDF, or Web URL formats) using the voices of unseen speakers. To enable long audio generation, we employ a language model with parameter, data, and context scaling to process sequences in an innovative format designed for modeling entire multi-speaker, multi-turn speech interactions. To enhance spontaneity, we observe that ASR transcripts capture spontaneous speech details (e.g., filler words indicating hesitations, and specific punctuation and spaces reflecting breathing pauses), suggesting that these transcripts can serve as a partial indicator of speech spontaneity. Building upon this assumption, we utilize a script generation module to generate scripts incorporating these spontaneous elements. Experiments show MoonCast outperforms baselines, with notable improvements in contextual coherence and spontaneity.

We propose unifying one-shot voice conversion and cloning into a single model that can be end-to-end optimized. To achieve this, we introduce a novel extension to a speech variational auto-encoder (VAE) that disentangles speech into content and speaker representations. Instead of using a fixed Gaussian prior as in the vanilla VAE, we incorporate a learnable text-aware prior as an informative guide for learning the content representation. This results in a content representation with reduced speaker information and more accurate linguistic information. The proposed model can sample the content representation using either the posterior conditioned on speech or the text-aware prior with textual input, enabling one-shot voice conversion and cloning, respectively. Experiments show that the proposed method achieves better or comparable overall performance for one-shot voice conversion and cloning compared to state-of-the-art voice conversion and cloning methods.

Audio generation is a major branch of generative AI research. Compared with prior works in this area that are commonly task-specific with heavy domain knowledge, this paper advocates building universal audio generation models that can handle various tasks in a unified manner. As recent research on large language models (LLMs) has demonstrated their strong ability to handle multiple tasks, this work presents UniAudio, an LLM-based audio generation model that supports a wide range of audio generation tasks. Based on various input conditions, such as phoneme, text description, or audio itself, UniAudio can generate speech, sound, music, and singing voice. The proposed UniAudio is built with 100k hours of multi-source open-available audio data and is scaled to 1B parameters. The audio tokenization method and language model architecture are also specifically designed for both performance and efficiency. Experimentally, UniAuido supports 11 audio generation tasks and achieves competitive results on all tasks consistently. We also show that UniAudio can support new tasks seamlessly via simple fine-tuning.

Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling. The ideal neural audio codec should maintain content, paralinguistics, speaker characteristics, and audio information even at low bitrates. Recently, numerous advanced neural codec models have been proposed. However, codec models are often tested under varying experimental conditions. As a result, we introduce the Codec-SUPERB challenge at SLT 20241, designed to facilitate fair and lightweight comparisons among existing codec models and inspire advancements in the field. This challenge brings together representative speech applications and objective metrics, and carefully selects license-free datasets, sampling them into small sets to reduce evaluation computation costs. This paper presents the challenge’s rules, datasets, participant systems, results, and findings.1https://codecsuperb.github.io/

Expressive text-to-speech (TTS) aims to synthesize speech with varying speaking styles to better reflect human speech patterns. In this study, we attempt to use natural language as a style prompt to control the styles in the synthetic speech,e.g., “Sigh tone in full of sad mood with some helpless feeling”. Considering that there is no existing TTS corpus that is suitable to benchmark this novel task, we first construct a speech corpus whose speech samples are annotated with not only content transcriptions but also style descriptions in natural language. Then we propose an expressive TTS model, named InstructTTS, which is novel in the sense of the following aspects: (1) We fully take advantage of self-supervised learning and cross-modal metric learning and propose a novel three-stage training procedure to obtain a robust sentence embedding model that can effectively capture semantic information from the style prompts and control the speaking style in the generated speech. (2) We propose to model acoustic features in discrete latent space and train a novel discrete diffusion probabilistic model to generate vector-quantized (VQ) acoustic tokens rather than the commonly-used mel spectrogram. (3) We jointly apply mutual information (MI) estimation and minimization during acoustic model training to minimize style-speaker and style-content MI, avoiding possible content and speaker information leakage from the style prompt. Extensive objective and subjective evaluation has been conducted to verify the effectiveness and expressiveness of InstructTTS. Experimental results show that InstructTTS can synthesize high-fidelity and natural speech with style prompts controlling the speaking style. Synthesized samples are available online.

We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual scientific event aiming to compare and understand different voice conversion (VC) systems based on a common dataset. This year we shifted our focus to singing voice conversion (SVC), thus named the challenge the Singing Voice Conversion Challenge (SVCC). A new database was constructed for two tasks, namely in-domain and cross-domain SVC. The challenge was run for two months, and in total we received 26 submissions, including 2 baselines. Through a large-scale crowd-sourced listening test, we observed that for both tasks, although human-level naturalness was achieved by the top system, no team was able to obtain a similarity score as high as the target speakers. Also, as expected, cross-domain SVC is harder than in-domain SVC, especially in the similarity aspect. We also investigated whether existing objective measurements were able to predict perceptual performance, and found that only few of them could reach a significant correlation.

Generative adversarial network (GAN)-based neural vocoders have been widely used in audio synthesis tasks due to their high generation quality, efficient inference, and small computation footprint. However, it is still challenging to train a universal vocoder which can generalize well to out-of-domain (OOD) scenarios, such as unseen speaking styles, non-speech vocalization, singing, and musical pieces. In this work, we propose SnakeGAN, a GAN-based universal vocoder, which can synthesize high-fidelity audio in various OOD scenarios. SnakeGAN takes a coarse-grained signal generated by a differentiable digital signal processing (DDSP) model as prior knowledge, aiming at recovering high-fidelity waveform from a Mel-spectrogram. We introduce periodic nonlinearities through the Snake activation function and anti-aliased representation into the generator, which further brings desired inductive bias for audio synthesis and significantly improves the extrapolation capacity for universal vocoding in unseen scenarios. To validate the effectiveness of our proposed method, we train SnakeGAN with only speech data and evaluate its performance for various OOD distributions with both subjective and objective metrics. Experimental results show that SnakeGAN significantly outperforms the compared approaches and can generate high-fidelity audio samples including unseen speakers with unseen styles, singing voices, instrumental pieces, and nonverbal vocalization.

Cross-speaker style transfer (CSST) in text-to-speech (TTS) synthesis aims at transferring a speaking style to the synthesised speech in a target speaker’s voice. Most previous CSST approaches rely on expensive high-quality data carrying desired speaking style during training and require a reference utterance to obtain speaking style descriptors as conditioning on the generation of a new sentence. This work presents Referee, a robust reference-free CSST approach for expressive TTS, which fully leverages low-quality data to learn speaking styles from text. Referee is built by cascading a text-to-style (T2S) model with a style-to-wave (S2W) model. Phonetic PosteriorGram (PPG), phoneme-level pitch and energy contours are adopted as fine-grained speaking style descriptors, which are predicted from text using the T2S model. A novel pretrain-refinement method is adopted to learn a robust T2S model by only using readily accessible low-quality data. The S2W model is trained with high-quality target data, which is adopted to effectively aggregate style descriptors and generate high-fidelity speech in the target speaker’s voice. Experimental results are presented, showing that Referee outperforms a global-style-token (GST)-based baseline approach in CSST.

Dysarthric speech reconstruction (DSR), which aims to improve the quality of dysarthric speech, remains a challenge, not only because we need to restore the speech to be normal, but also must preserve the speaker’s identity. The speaker representation extracted by the speaker encoder (SE) optimized for speaker verification has been explored to control the speaker identity. However, the SE may not be able to fully capture the characteristics of dysarthric speakers that are previously unseen. To address this research problem, we propose a novel multi-task learning strategy, i.e., adversarial speaker adaptation (ASA). The primary task of ASA fine-tunes the SE with the speech of the target dysarthric speaker to effectively capture identityrelated information, and the secondary task applies adversarial training to avoid the incorporation of abnormal speaking patterns into the reconstructed speech, by regularizing the distribution of reconstructed speech to be close to that of reference speech with high quality. Experiments show that the proposed approach can achieve enhanced speaker similarity and comparable speech naturalness with a strong baseline approach. Compared with dysarthric speech, the reconstructed speech achieves 22.3% and 31.5% absolute word error rate reduction for speakers with moderate and moderate-severe dysarthria respectively. Our demo page is released here1.

Singing voice conversion (SVC) is one promising technique that can enrich the way of human-computer interaction by en-dowing a computer the ability to produce high-fidelity and expressive singing voice. In this paper, we propose DiffSVC, an SVC system based on denoising diffusion probabilistic model. DiffSVC uses phonetic posteriorgrams (PPGs) as con-tent features. A denoising module is trained in DiffSVC, which takes destroyed mel spectrogram produced by the dif-fusion/forward process and its corresponding step information as input to predict the added Gaussian noise. We use PPGs, fundamental frequency features and loudness features as auxiliary inputs to assist the denoising process. Experi-ments show that DiffSVC can achieve superior conversion performance in terms of naturalness and voice similarity to current state-of-the-art SVC approaches.

This paper presents FastSVC, a light-weight cross-domain singing voice conversion (SVC) system, which can achieve high conversion performance, with inference speed 4x faster than real-time on CPUs. FastSVC uses Conformer-based phoneme recognizer to extract singer-agnostic linguistic features from singing signals. A feature-wise linear modulation based generator is used to synthesize waveform directly from linguistic features, leveraging information from sine-excitation signals and loudness features. The waveform generator can be trained conveniently using a multi-resolution spectral loss and an adversarial loss. Experimental results show that the proposed FastSVC system, compared with a computationally heavy baseline system, can achieve comparable conversion performance in some scenarios and significantly better conversion performance in other scenarios. Moreover, the proposed FastSVC system achieves desirable cross-lingual singing conversion performance. The inference speed of the FastSVC system is 3x and 70x faster than the baseline system on GPUs and CPUs, respectively.

Though significant progress has been made for the voice conversion (VC) of typical speech, VC for atypical speech, e.g., dysarthric and second-language (L2) speech, remains a challenge, since it involves correcting for atypical prosody while maintaining speaker identity.To address this issue, we propose a VC system with explicit prosodic modelling and deep speaker embedding (DSE) learning.First, a speech-encoder strives to extract robust phoneme embeddings from atypical speech.Second, a prosody corrector takes in phoneme embeddings to infer typical phoneme duration and pitch values.Third, a conversion model takes phoneme embeddings and typical prosody features as inputs to generate the converted speech, conditioned on the target DSE that is learned via speaker encoder or speaker adaptation.Extensive experiments demonstrate that speaker adaptation can achieve higher speaker similarity, and the speaker encoder based conversion model can greatly reduce dysarthric and non-native pronunciation patterns with improved speech intelligibility.A comparison of speech recognition results between the original dysarthric speech and converted speech show that absolute reduction of 47.6% character error rate (CER) and 29.3% word error rate (WER) can be achieved.

This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach, which utilizes text supervision during training. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module. During the training stage, an encoder-decoder-based hybrid connectionist-temporal-classification-attention (CTC-attention) phoneme recognizer is trained, whose encoder has a bottle-neck layer. A BNE is obtained from the phoneme recognizer and is utilized to extract speaker-independent, dense and rich spoken content representations from spectral features. Then a multi-speaker location-relative attention based seq2seq synthesis model is trained to reconstruct spectral features from the bottle-neck features, conditioning on speaker representations for speaker identity control in the generated speech. To mitigate the difficulties of using seq2seq models to align long sequences, we down-sample the input spectral feature along the temporal dimension and equip the synthesis model with a discretized mixture of logistic (MoL) attention mechanism. Since the phoneme recognizer is trained with large speech recognition data corpus, the proposed approach can conduct any-to-many voice conversion. Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity. Ablation studies are conducted to confirm the effectiveness of feature selection and model design strategies in the proposed approach. The proposed VC approach can readily be extended to support any-to-any VC (also known as one/few-shot VC), and achieve high performance according to objective and subjective evaluations.

Expressive text-to-speech (E-TTS) synthesis is important for enhancing user experience in communication with machines using the speech modality. However, one of the challenges in E-TTS is the lack of a precise description of emotions. Previous categorical specifications may be insufficient for describing complex emotions. The dimensional specifications face the difficulty of ambiguity in annotation. This work advocates a new approach of describing emotive speech acoustics using spoken exemplars. We investigate methods to extract emotion descriptions from the input exemplar of emotive speech. The measures are combined to form two descriptors, based on capsule network (CapNet) and residual error network (RENet). The first is designed to consider the spatial information in the input exemplary spectrogram, and the latter is to capture the contrastive information between emotive acoustic expressions. Two different approaches are applied for conversion from the variable-length feature sequence to fixed-size description vector: (1) dynamic routing groups similar capsules to the output description; and (2) recurrent neural network's hidden states store the temporal information for the description. The two descriptors are integrated to a state-of-the-art sequence-to-sequence architecture to obtain an end-to-end architecture that is optimized as a whole towards the same goal of generating correct emotive speech. Experimental results on a public audiobook dataset demonstrate that the two exemplar-based approaches achieve significant performance improvement over the baseline system in both emotion similarity and speech quality.

Speech emotion recognition (SER) is an indispensable part of fluid human-machine interaction and attracts lots of research attentions. Recent work on SER has successfully applied convolutional neural networks (CNNs) to learn feature representations from speech spectrograms. However, the fundamental problem of CNNs is that the spatial information in spectrograms is lost, which includes positional and relationship information of low-level features, such as pitch and formant frequencies. We propose a novel architecture of sequential capsule networks (CapNets) by leveraging the advantange of CapNets that spatial information can be preserved in capsules and passed to upper capsule layers via dynamic routing. Also, the dynamic routing algorithm provides an effective alternative to pooling or storing recurrent hidden states for obtaining utterance-level features from the sequential capsule outputs. To further improve the model's ability to capture contextual information, we introduce a recurrent connection to the sequential structure. The experimental comparison of the proposed systems and previously published systems using CNNs and recurrent neural networks (RNNs) based on the IEMOCAP corpus demonstrates the effectiveness of the proposed sequential CapNets.

Multi-modality fusion technologies have greatly improved the performance of neural network-based Video Description/Caption, Visual Question Answering (VQA) and Audio Visual Scene-aware Dialog (AVSD) over the recent years. Most previous approaches only explore the last layers of multiple layer feature fusion while omitting the importance of intermediate layers. To solve the issue for the intermediate layers, we propose an efficient Quaternion Block Network (QBN) to learn interaction not only for the last layer but also for all intermediate layers simultaneously. In our proposed QBN, we use the holistic text features to guide the update of visual features. In the meantime, Hamilton quaternion products can efficiently perform information flow from higher layers to lower layers for both visual and text modalities. The evaluation results show our QBN improve the performance on VQA 2.0, furthermore surpasses the approach using large scale BERT or visual BERT pre-trained models. Extensive ablation study has been carried out to examine the influence of each proposed module in this study.

Synthesizing fluent code-switched (CS) speech with consistent voice using only monolingual corpora is still a challenging task, since language alternation seldom occurs during training and the speaker identity is directly correlated with language. In this paper, we present a bilingual phonetic posteriorgram (PPG) based CS speech synthesizer using only monolingual corpora. The bilingual PPG is used to bridge across speakers and languages, which is formed by stacking two monolingual PPGs extracted from two monolingual speaker-independent speech recognition systems. It is assumed that bilingual PPG can represent the articulation of speech sounds speaker-independently and captures accurate phonetic information of both languages in the same feature space. The proposed model first extracts bilingual PPGs from training data. Then an encoder- decoder based model is used to learn the relationship between input text and bilingual PPGs, and the bilingual PPGs are mapped to acoustic features using bidirectional long-short term memory based model conditioned on speaker embedding to control speaker identity. Experiments validate the effectiveness of the proposed model in terms of speech intelligibility, audio fidelity and speaker consistency of the generated code-switched speech.

Techniques for accent conversion (AC) aim to convert non-native to native accented speech. Conventional AC methods try to convert only the speaker identity of a native speaker's voice to that of the non-native accented target speaker, leaving the underlying content and pronunciations unchanged. This hinders their practical use in real-world applications, because native-accented utterances are required at conversion stage. In this paper, we present an end-to-end framework, which is able to conduct AC from non-native-accented utterances without using any native-accented utterances during online conversion. We achieve this by independently extracting linguistic and speaker representations from non-native accented speech and condition a speech synthesis model on these representations to generate native-accented speech. Experiments on open-source data corpora show that the proposed system can convert Hindi-accented English speech into native American English speech with high naturalness, which is indistinguishable from native-accented recordings in terms of accent.

Dysarthric speech reconstruction (DSR) is a challenging task due to difficulties in repairing unstable prosody and correcting imprecise articulation. Inspired by the success of sequence-to-sequence (seq2seq) based text-to-speech (TTS) synthesis and knowledge distillation (KD) techniques, this paper proposes a novel end-to-end voice conversion (VC) method to tackle the reconstruction task. The proposed approach contains three components. First, a seq2seq based TTS is first trained with the transcribed normal speech. Second, with the text-encoder of this trained TTS system as "teacher", a teacher-student framework is proposed for cross-modal KD by training a speech-encoder to extract appropriate linguistic representations from the transcribed dysarthric speech. Third, the speech-encoder of the previous component is concatenated with the attention and decoder of the first component (TTS) to perform the DSR task, by directly mapping the dysarthric speech to its normal version. Experiments demonstrate that the proposed method can generate the speech with high naturalness and intelligibility, where the comparisons of human speech recognition between the reconstructed speech and the original dysarthric speech show that 35.4% and 48.7% absolute word error rate (WER) reduction can be achieved for dysarthric speakers with low and very low speech intelligibility, respectively.

Various forefront countermeasure methods for automatic speaker verification (ASV) with considerable performance in anti-spoofing are proposed in the ASVspoof 2019 challenge. However, previous work has shown that countermeasure models are vulnerable to adversarial examples indistinguishable from natural data. A good countermeasure model should not only be robust against spoofing audio, including synthetic, converted, and replayed audios; but counteract deliberately generated examples by malicious adversaries. In this work, we introduce a passive defense method, spatial smoothing, and a proactive defense method, adversarial training, to mitigate the vulnerability of ASV spoofing countermeasure models against adversarial examples. This paper is among the first to use defense methods to improve the robustness of ASV spoofing countermeasure models under adversarial attacks. The experimental results show that these two defense methods positively help spoofing countermeasure models counter adversarial examples.

Voice conversion (VC) techniques aim to modify speaker identity of an utterance while preserving the underlying linguistic information.Most VC approaches ignore modeling of the speaking style (e.g.emotion and emphasis), which may contain the factors intentionally added by the speaker and should be retained during conversion.This study proposes a sequence-tosequence based non-parallel VC approach, which has the capability of transferring the speaking style from the source speech to the converted speech by explicitly modeling.Objective evaluation and subjective listening tests show superiority of the proposed VC approach in terms of speech naturalness and speaker similarity of the converted speech.Experiments are also conducted to show the source-style transferability of the proposed approach.

High-performance spoofing countermeasure systems for automatic speaker verification (ASV) have been proposed in the ASVspoof 2019 challenge. However, the robustness of such systems under adversarial attacks has not been studied yet. In this paper, we investigate the vulnerability of spoofing countermeasures for ASV under both white-box and black-box adversarial attacks with the fast gradient sign method (FGSM) and the projected gradient descent (PGD) method. We implement high-performing countermeasure models in the ASVspoof 2019 challenge and conduct adversarial attacks on them. We compare performance of black-box attacks across spoofing countermeasure models with different network architectures and different amount of model parameters. The experimental results show that all implemented countermeasure models are vulnerable to FGSM and PGD attacks under the scenario of white-box attack. The more dangerous black-box attacks also prove to be effective by the experimental results.

State-of-the-art text-to-speech (TTS) synthesis models can produce monolingual speech with high intelligibility and naturalness. However, when the models are applied to synthesize code-switched (CS) speech, the performance declines seriously. Conventionally, developing a CS TTS system requires multilingual data to incorporate language-specific and cross-lingual knowledge. Recently, end-to-end (E2E) architecture has achieved satisfactory results in monolingual TTS. The architecture enables the training from one end of alphabetic text input to the other end of acoustic feature output. In this paper, we explore the use of E2E framework for CS TTS, using a combination of Mandarin and English monolingual speech corpus uttered by two female speakers. To handle alphabetic input from different languages, we explore two kinds of encoders: (1) shared multilingual encoder with explicit language embedding (LDE); (2) separated monolingual encoder (SPE) for each language. The two systems use identical decoder architecture, where a discriminative code is incorporated to enable the model to generate speech in one speaker's voice consistently. Experiments confirm the effectiveness of the proposed modifications on the E2E TTS framework in terms of quality and speaker similarity of the generated speech. Moreover, our proposed systems can generate controllable foreign-accented speech at character-level using only mixture of monolingual training data.

Speech emotion recognition (SER) is a fundamental step towards fluent human-machine interaction. One challenging problem in SER is obtaining utterance-level feature representation for classification. Recent works on SER have made significant progress by using spectrogram features and introducing neural network methods, e.g., convolutional neural networks (CNNs). However the fundamental problem of CNNs is that the spatial information in spectrograms is not captured, which are basically position and relationship information of low-level features like pitch and formant frequencies. This paper presents a novel architecture based on the capsule networks (CapsNets) for SER. The proposed system can take into account the spatial relationship of speech features in spectrograms, and provide an effective pooling method for obtaining utterance global features. We also introduce a recurrent connection to CapsNets to improve the model's time sensitivity. We compare the proposed model to previous published results based on combined CNN-long short-term memory (CNN-LSTM) models on the benchmark corpus IEMOCAP over four emotions, i.e., neutral, angry, happy and sad. Experimental results show that our model achieves better results than the baseline system on weighted accuracy (WA) (72.73% vs. 68.8%) and un-weighted accuracy (UA) (59.71% vs. 59.4%), which demonstrates the effectiveness of CapsNets for SER.

Recurrent neural network language models (RNNLMs) have become an increasing popular choice for state-of-the-art speech recognition systems. RNNLMs are normally trained by minimizing the cross entropy (CE) using the stochastic gradient descent (SGD) algorithm. However, the SGD method doesn't consider the correlation between parameters and therefore can lead to unstable and slow convergence in training. Second-order optimization methods provide a possible solution to this issue. However these methods are either computationally heavy or do not have competitive performance. In this paper, a novel optimization method - stochastic natural gradient based on minimum variance assumption (SNGM) is proposed for training RNNLMs. It allows the natural gradient method to operate at a comparable training efficiency to the SGD method. By modifying the gradient according to the local curvature of the KL-divergence between current and updated probabilistic distributions, the proposed SNGM approach is shown to outperform both the SGD and limited memory BFGS methods across three tasks: Penn Treebank, Switchboard conversational speech recognition and AMI meeting room transcription in terms of both perplexity and word error rate.

Speaking style plays an important role in the expressivity of speech for communication. Hence speaking style is very important for synthetic speech as well. Speaking style adaptation faces the difficulty that the data of specific styles may be limited and difficult to obtain in large amounts. A possible solution is to leverage data from speaking styles that are more available, to train the speech synthesizer and then adapt it to the target style for which the data is scarce. Conventional DNN adaptation approaches directly update the top layers of a well-trained, style-dependent model towards the target style. The detailed local context-level mismatch between the original and the target styles is not considered. In order to address this issue, two frame-level input feature-based style adaptation techniques are investigated in this paper. We will use style features extracted from (1) a target-style data trained bottleneck DNN, and (2) a novel cross-style residual feature regression DNN. These features are used for top-layer adaptation of a well-trained style-dependent synthesis network. Experimental results on adapting the declarative sty le to the interrogative sty le demonstrate the effectiveness of our proposed style features in improving the expressiveness of synthesizing speech for the interrogative style, while maintaining speech quality.