1.9k citations · 3.6k across the 72 of their papers we have counts for
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SpeechX: Neural Codec Language Model as a Versatile Speech Transformer
Xiaofei Wang, Manthan Thakker, Zhuo Chen +7
Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech. However, existing models…
On decoder-only architecture for speech-to-text and large language model integration
Jian Wu, Yashesh Gaur, Zhuo Chen +8
Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. Howeve…
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers
Chenda Li, Yao Qian, Zhuo Chen +5
State-of-the-art large-scale universal speech models (USMs) show a decent automatic speech recognition (ASR) performance across multiple domains and languages. However, it remains…
Target Sound Extraction with Variable Cross-modality Clues
Chenda Li, Yao Qian, Zhuo Chen +5
Automatic target sound extraction (TSE) is a machine learning approach to mimic the human auditory perception capability of attending to a sound source of interest from a mixture o…
Speaker Change Detection for Transformer Transducer ASR
Jian Wu, Zhuo Chen, Min Hu +2
Speaker change detection (SCD) is an important feature that improves the readability of the recognized words from an automatic speech recognition (ASR) system by breaking the word…
BEATs: Audio Pre-Training with Acoustic Tokenizers
Sanyuan Chen, Yu Wu, Chengyi Wang +4
The massive growth of self-supervised learning (SSL) has been witnessed in language, vision, speech, and audio domains over the past few years. While discrete label prediction is w…
Speech separation with large-scale self-supervised learning
Zhuo Chen, Naoyuki Kanda, Jian Wu +6
Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the ex…
Exploring WavLM on Speech Enhancement
Hyungchan Song, Sanyuan Chen, Zhuo Chen +5
There is a surge in interest in self-supervised learning approaches for end-to-end speech encoding in recent years as they have achieved great success. Especially, WavLM showed sta…
Simulating realistic speech overlaps improves multi-talker ASR
Muqiao Yang, Naoyuki Kanda, Xiaofei Wang +5
Multi-talker automatic speech recognition (ASR) has been studied to generate transcriptions of natural conversation including overlapping speech of multiple speakers. Due to the di…
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement
Tianrui Wang, Xie Chen, Zhuo Chen +2
In recent years, self-supervised learning (SSL) has achieved tremendous success in various speech tasks due to its power to extract representations from massive unlabeled data. How…
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition
Zili Huang, Zhuo Chen, Naoyuki Kanda +6
Self-supervised learning (SSL), which utilizes the input data itself for representation learning, has achieved state-of-the-art results for various downstream speech tasks. However…
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts
Xiaofei Wang, Zhuo Chen, Yu Shi +3
Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs. First, while a larger model impr…
VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition
Naoyuki Kanda, Jian Wu, Xiaofei Wang +3
This paper presents a novel streaming automatic speech recognition (ASR) framework for multi-talker overlapping speech captured by a distant microphone array with an arbitrary geom…
Ultra Fast Speech Separation Model with Teacher Student Learning
Sanyuan Chen, Yu Wu, Zhuo Chen +5
Transformer has been successfully applied to speech separation recently with its strong long-dependency modeling capacity using a self-attention mechanism. However, Transformer ten…
Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings
Naoyuki Kanda, Jian Wu, Yu Wu +7
This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR) model that can recognize ``who spoke what'' with low latency even when multiple people are…
Streaming Multi-Talker ASR with Token-Level Serialized Output Training
Naoyuki Kanda, Jian Wu, Yu Wu +7
This paper proposes a token-level serialized output training (t-SOT), a novel framework for streaming multi-talker automatic speech recognition (ASR). Unlike existing streaming mul…
Separating Long-Form Speech with Group-Wise Permutation Invariant Training
Wangyou Zhang, Zhuo Chen, Naoyuki Kanda +8
Multi-talker conversational speech processing has drawn many interests for various applications such as meeting transcription. Speech separation is often required to handle overlap…
Continuous Speech Separation with Recurrent Selective Attention Network
Yixuan Zhang, Zhuo Chen, Jian Wu +4
While permutation invariant training (PIT) based continuous speech separation (CSS) significantly improves the conversation transcription accuracy, it often suffers from speech lea…
VarArray: Array-Geometry-Agnostic Continuous Speech Separation
Takuya Yoshioka, Xiaofei Wang, Dongmei Wang +4
Continuous speech separation using a microphone array was shown to be promising in dealing with the speech overlap problem in natural conversation transcription. This paper propose…
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka +3
With the recent surge of video conferencing tools usage, providing high-quality speech signals and accurate captions have become essential to conduct day-to-day business or connect…
Personalized Speech Enhancement: New Models and Comprehensive Evaluation
Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang +3
Personalized speech enhancement (PSE) models utilize additional cues, such as speaker embeddings like d-vectors, to remove background noise and interfering speech in real-time and…
All-neural beamformer for continuous speech separation
Zhuohuang Zhang, Takuya Yoshioka, Naoyuki Kanda +4
Continuous speech separation (CSS) aims to separate overlapping voices from a continuous influx of conversational audio containing an unknown number of utterances spoken by an unkn…
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR
Naoyuki Kanda, Xiong Xiao, Yashesh Gaur +4
This paper presents Transcribe-to-Diarize, a new approach for neural speaker diarization that uses an end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR). The…
Continuous Streaming Multi-Talker ASR with Dual-path Transducers
Desh Raj, Liang Lu, Zhuo Chen +2
Streaming recognition of multi-talker conversations has so far been evaluated only for 2-speaker single-turn sessions. In this paper, we investigate it for multi-turn meetings cont…