Speech editing has garnered more and more attention due to its diverse applications. However, existing systems often require substantial manual effort or have limited capabilities in attribute editing, imposing significant constraints. In this work, we present SpeechPalette, a comprehensive high-quality speech editing method that allows users to easily modify various attributes of the selected speech segment according to their preferences. Specifically, the proposed model approaches speech editing from a decoupling perspective, disentangling critical information such as text, pitch, duration and more from the input speech. Then, reconstruction is achieved through a mask and prediction mechanism. Furthermore, we leverage a diffusion model to predict the residuals between the real and predicted speech, further enhancing synthesis quality. The proposed method not only excels at text-based speech editing but also handles tasks involving pitch and speed rate adjustments. Moreover, it also demonstrates remarkable performance in one-shot text-to-speech scenarios. While recent large-scale models achieve impressive synthesis quality through massive computational resources, SpeechPalette offers a balanced approach with explicit fine-grained control over speech attributes, practical deployment requirements, and competitive performance relative to similarly-sized systems. Experimental results across a range of tasks consistently demonstrate the superior performance of our method compared to baseline systems. Additionally, comprehensive ablation studies validate the effectiveness of our proposed approach.

Recent advancements in generative modeling have significantly enhanced the reconstruction of audio waveforms from various representations. While diffusion models are adept at this task, they are hindered by latency issues due to their operation at the individual sample point level and the need for numerous sampling steps. In this study, we introduce RFWave, a cutting-edge multi-band Rectified Flow approach designed to reconstruct high-fidelity audio waveforms from Mel-spectrograms or discrete acoustic tokens. RFWave uniquely generates complex spectrograms and operates at the frame level, processing all subbands simultaneously to boost efficiency. Leveraging Rectified Flow, which targets a straight transport trajectory, RFWave achieves reconstruction with just 10 sampling steps. Our empirical evaluations show that RFWave not only provides outstanding reconstruction quality but also offers vastly superior computational efficiency, enabling audio generation at speeds up to 160 times faster than real-time on a GPU. Both an online demonstration and the source code are accessible.

Voice cloning, also known as personalized voice synthesis, is a significant branch of speech synthesis. It involves synthesizing speech with the same vocal characteristics as the target speaker. The field of voice cloning mainly faces two challenges: 1. Typically, only a small amount of voice data from the target speaker is available, necessitating the cloning of the speaker’s timbre using limited available data; 2. The voice data of the target speaker is often recorded with non-professional equipment in noisy environments, posing a significant challenge to the robustness of voice cloning systems. In this paper, we propose a noise-robust voice adaptation method based on speaker-independent bottleneck features.The method contains: 1. Using a disentanglement module based on autoencoder architecture to disentangle the timbre information from the speech data, resulting in the extraction of corresponding speaker-independent bottleneck features with environmental information; 2. With the help of the above module, the Text2BN (Text to Bottleneck) module is trained with high-quality voice data to establish a mapping from text to clean speaker-independent bottleneck features; 3. The decoder is fine-tuned using noisy target speaker speech data to adapt to the target speaker and is cascaded with the Text2BN module to synthesize clean audio. The disentanglement module does not require text transcriptions and not mandate the use of artificially paired clean/noisy datasets, enabling large-scale pre-training with massive real-world, untranscribed, noisy datasets to further enhance the model’s noise robustness and capabilities of decoupling timbre. During the cloning phase, there are no requirements for the recording conditions of the target speaker’s speech data or for text transcriptions, aligning more closely with real application scenarios. Since our model does not directly work on the noise level, it effectively avoids issues of insufficient robustness to out-of-distribution noise. Experiments demonstrate that the method can effectively utilize noisy target speaker speech data for voice cloning, achieving the preponderance of both speech quality and similarity in the synthesized speech.

With the increasing popularity of speech synthesis products, the industry has put forward more requirements for personalized speech synthesis: (1) How to use low-resource, easily accessible data to clone a person’s voice. (2) How to clone a person’s voice while controlling the style and prosody. To solve the above two problems, we proposed the Hieratron model framework in which the prosody and timbre are modeled separately using two modules, therefore, the independent control of timbre and the other characteristics of audio can be achieved while generating speech. The practice shows that, for very limited target speaker data in the wild, Hieratron has obvious advantages over the traditional method, in addition to controlling the style and language of the generated speech, the mean opinion score on speech quality of the generated speech has also been improved by more than 0.2 points.

Neural text-to-speech (TTS) approaches generally require a huge number of high quality speech data, which makes it difficult to obtain such a dataset with extra emotion labels. In this paper, we propose a novel approach for emotional TTS synthesis on a TTS dataset without emotion labels. Specifically, our proposed method consists of a cross-domain speech emotion recognition (SER) model and an emotional TTS model. Firstly, we train the cross-domain SER model on both SER and TTS datasets. Then, we use emotion labels on the TTS dataset predicted by the trained SER model to build an auxiliary SER task and jointly train it with the TTS model. Experimental results show that our proposed method can generate speech with the specified emotional expressiveness and nearly no hindering on the speech quality.

Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition is difficult, as emotions are ambiguous. We propose a novel approach to learn discriminative features from variable length spectrograms for emotion recognition by cooperating soft-max cross-entropy loss and center loss together. The soft-max cross-entropy loss enables features from different emotion categories separable, and center loss efficiently pulls the features belonging to the same emotion category to their center. By combining the two losses together, the discriminative power will be highly enhanced, which leads to network learning more effective features for emotion recognition. As demonstrated by the experimental results, after introducing center loss, both the unweighted accuracy and weighted accuracy are improved by over 3% on Mel-spectrogram input, and more than 4% on Short Time Fourier Transform spectrogram input.

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.