85 citations · 632 across the 65 of their papers we have counts for
13 papers · 1 filter
Synchronous Transformers for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
For most of the attention-based sequence-to-sequence models, the decoder predicts the output sequence conditioned on the entire input sequence processed by the encoder. The asynchr…
Integrating Knowledge into End-to-End Speech Recognition from External Text-Only Data
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because of the end-to-end training, an AED model is usually trained…
Domain adversarial learning for emotion recognition
Zheng Lian, Jianhua Tao, Bin Liu +1
In practical applications for emotion recognition, users do not always exist in the training corpus. The mismatch between training speakers and testing speakers affects the perform…
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition
Zheng Lian, Jianhua Tao, Bin Liu +1
Prior works on speech emotion recognition utilize various unsupervised learning approaches to deal with low-resource samples. However, these methods pay less attention to modeling…
Conversational Emotion Analysis via Attention Mechanisms
Zheng Lian, Jianhua Tao, Bin Liu +1
Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational…
Expression Analysis Based on Face Regions in Read-world Conditions
Zheng Lian, Ya Li, Jian-Hua Tao +2
Facial emotion recognition is an essential and important aspect of the field of human-machine interaction. Past research on facial emotion recognition focuses on the laboratory env…
Speech Emotion Recognition via Contrastive Loss under Siamese Networks
Zheng Lian, Ya Li, Jianhua Tao +1
Speech emotion recognition is an important aspect of human-computer interaction. Prior work proposes various end-to-end models to improve the classification performance. However, m…
Towards Fine-Grained Prosody Control for Voice Conversion
Zheng Lian, Zhengqi Wen
In a typical voice conversion system, prior works utilize various acoustic features (e.g., the pitch, voiced/unvoiced flag, aperiodicity) of the source speech to control the prosod…
Self-Attention Transducers for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao +2
Recurrent neural network transducers (RNN-T) have been successfully applied in end-to-end speech recognition. However, the recurrent structure makes it difficult for parallelizatio…
Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features
Cunhang Fan, Bin Liu, Jianhua Tao +2
Deep clustering (DC) and utterance-level permutation invariant training (uPIT) have been demonstrated promising for speaker-independent speech separation. DC is usually formulated…
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…
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition
Ye Bai, Jiangyan Yi, Jianhua Tao +2
Integrating an external language model into a sequence-to-sequence speech recognition system is non-trivial. Previous works utilize linear interpolation or a fusion network to inte…
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation
Jia Li, Xiao Sun, Xing Wei +2
In recent years, the generation of conversation content based on deep neural networks has attracted many researchers. However, traditional neural language models tend to generate g…