8 citations · 20 across the 25 of their papers we have counts for
8 papers · 1 filter
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation
Jiaxu Zhu, Weinan Tong, Yaoxun Xu +6
Mapping two modalities, speech and text, into a shared representation space, is a research topic of using text-only data to improve end-to-end automatic speech recognition (ASR) pe…
Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator
Lingwei Meng, Jiawen Kang, Mingyu Cui +3
Multi-talker overlapped speech poses a significant challenge for speech recognition and diarization. Recent research indicated that these two tasks are inter-dependent and compleme…
Inter-SubNet: Speech Enhancement with Subband Interaction
Jun Chen, Wei Rao, Zilin Wang +5
Subband-based approaches process subbands in parallel through the model with shared parameters to learn the commonality of local spectrums for noise reduction. In this way, they ha…
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One
Lingwei Meng, Jiawen Kang, Mingyu Cui +3
Although automatic speech recognition (ASR) can perform well in common non-overlapping environments, sustaining performance in multi-talker overlapping speech recognition remains c…
Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition
Shujie Hu, Xurong Xie, Zengrui Jin +6
Automatic recognition of disordered and elderly speech remains a highly challenging task to date due to the difficulty in collecting such data in large quantities. This paper explo…
Speech Enhancement with Fullband-Subband Cross-Attention Network
Jun Chen, Wei Rao, Zilin Wang +5
FullSubNet has shown its promising performance on speech enhancement by utilizing both fullband and subband information. However, the relationship between fullband and subband in F…
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…
Towards Green ASR: Lossless 4-bit Quantization of a Hybrid TDNN System on the 300-hr Switchboard Corpus
Junhao Xu, Shoukang Hu, Xunying Liu +1
State of the art time automatic speech recognition (ASR) systems are becoming increasingly complex and expensive for practical applications. This paper presents the development of…