185 citations · 501 across the 25 of their papers we have counts for
25 papers
ContextSpeech: Expressive and Efficient Text-to-Speech for Paragraph Reading
Yujia Xiao, Shaofei Zhang, Xi Wang +5
While state-of-the-art Text-to-Speech systems can generate natural speech of very high quality at sentence level, they still meet great challenges in speech generation for paragrap…
A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS
Haohan Guo, Fenglong Xie, Frank K. Soong +2
We propose a Multi-Stage, Multi-Codebook (MSMC) approach to high-performance neural TTS synthesis. A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is us…
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS
Liumeng Xue, Frank K. Soong, Shaofei Zhang +1
Recent advancements in neural end-to-end TTS models have shown high-quality, natural synthesized speech in a conventional sentence-based TTS. However, it is still challenging to re…
NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality
Xu Tan, Jiawei Chen, Haohe Liu +11
Text to speech (TTS) has made rapid progress in both academia and industry in recent years. Some questions naturally arise that whether a TTS system can achieve human-level quality…
Disentangling Style and Speaker Attributes for TTS Style Transfer
Xiaochun An, Frank K. Soong, Lei Xie
End-to-end neural TTS has shown improved performance in speech style transfer. However, the improvement is still limited by the available training data in both target styles and sp…
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge
Mutian He, Jingzhou Yang, Lei He +1
End-to-end TTS requires a large amount of speech/text paired data to cover all necessary knowledge, particularly how to pronounce different words in diverse contexts, so that a neu…
An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings
Wenxuan Ye, Shaoguang Mao, Frank Soong +4
Many mispronunciation detection and diagnosis (MD&D) research approaches try to exploit both the acoustic and linguistic features as input. Yet the improvement of the performance i…
A Survey on Neural Speech Synthesis
Xu Tan, Tao Qin, Frank Soong +1
Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural speech given text, is a hot research topic in speech, language, and machine learning co…
Improving Performance of Seen and Unseen Speech Style Transfer in End-to-end Neural TTS
Xiaochun An, Frank K. Soong, Lei Xie
End-to-end neural TTS training has shown improved performance in speech style transfer. However, the improvement is still limited by the training data in both target styles and spe…
Speech BERT Embedding For Improving Prosody in Neural TTS
Liping Chen, Yan Deng, Xi Wang +2
This paper presents a speech BERT model to extract embedded prosody information in speech segments for improving the prosody of synthesized speech in neural text-to-speech (TTS). A…
Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis
Mutian He, Jingzhou Yang, Lei He +1
To scale neural speech synthesis to various real-world languages, we present a multilingual end-to-end framework that maps byte inputs to spectrograms, thus allowing arbitrary inpu…
MBNet: MOS Prediction for Synthesized Speech with Mean-Bias Network
Yichong Leng, Xu Tan, Sheng Zhao +3
Mean opinion score (MOS) is a popular subjective metric to assess the quality of synthesized speech, and usually involves multiple human judges to evaluate each speech utterance. T…
s-Transformer: Segment-Transformer for Robust Neural Speech Synthesis
Xi Wang, Huaiping Ming, Lei He +1
Neural end-to-end text-to-speech (TTS) , which adopts either a recurrent model, e.g. Tacotron, or an attention one, e.g. Transformer, to characterize a speech utterance, has achiev…
Improving pronunciation assessment via ordinal regression with anchored reference samples
Bin Su, Shaoguang Mao, Frank Soong +3
Sentence level pronunciation assessment is important for Computer Assisted Language Learning (CALL). Traditional speech pronunciation assessment, based on the Goodness of Pronuncia…
Transfer Learning for Improving Singing-voice Detection in Polyphonic Instrumental Music
Yuanbo Hou, Frank K. Soong, Jian Luan +1
Detecting singing-voice in polyphonic instrumental music is critical to music information retrieval. To train a robust vocal detector, a large dataset marked with vocal or non-voca…
Conversational End-to-End TTS for Voice Agent
Haohan Guo, Shaofei Zhang, Frank K. Soong +2
End-to-end neural TTS has achieved superior performance on reading style speech synthesis. However, it's still a challenge to build a high-quality conversational TTS due to the lim…
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network
Min-Jae Hwang, Eunwoo Song, Ryuichi Yamamoto +2
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN). The recently proposed LPCNet vocoder has successfully…
Forward-Backward Decoding for Regularizing End-to-End TTS
Yibin Zheng, Xi Wang, Lei He +4
Neural end-to-end TTS can generate very high-quality synthesized speech, and even close to human recording within similar domain text. However, it performs unsatisfactory when scal…
A New GAN-based End-to-End TTS Training Algorithm
Haohan Guo, Frank K. Soong, Lei He +1
End-to-end, autoregressive model-based TTS has shown significant performance improvements over the conventional one. However, the autoregressive module training is affected by the…
Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS
Haohan Guo, Frank K. Soong, Lei He +1
The end-to-end TTS, which can predict speech directly from a given sequence of graphemes or phonemes, has shown improved performance over the conventional TTS. However, its predict…
Feature reinforcement with word embedding and parsing information in neural TTS
Huaiping Ming, Lei He, Haohan Guo +1
In this paper, we propose a feature reinforcement method under the sequence-to-sequence neural text-to-speech (TTS) synthesis framework. The proposed method utilizes the multiple i…
Modeling Multi-speaker Latent Space to Improve Neural TTS: Quick Enrolling New Speaker and Enhancing Premium Voice
Yan Deng, Lei He, Frank Soong
Neural TTS has shown it can generate high quality synthesized speech. In this paper, we investigate the multi-speaker latent space to improve neural TTS for adapting the system to…
LP-WaveNet: Linear Prediction-based WaveNet Speech Synthesis
Min-Jae Hwang, Frank Soong, Eunwoo Song +3
We propose a linear prediction (LP)-based waveform generation method via WaveNet vocoding framework. A WaveNet-based neural vocoder has significantly improved the quality of parame…
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding
Peilu Wang, Yao Qian, Frank K. Soong +2
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or…