28 citations · 78 across the 16 of their papers we have counts for
16 papers
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition
Qijie Shao, Linhao Dong, Kun Wei +2
Data2vec is a self-supervised learning (SSL) approach that employs a teacher-student architecture for contextual representation learning via masked prediction, demonstrating remark…
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training
Minglun Han, Ye Bai, Chen Shen +6
Speech self-supervised pre-training can effectively improve the performance of downstream tasks. However, previous self-supervised learning (SSL) methods for speech, such as HuBERT…
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Ye Bai, Jingping Chen, Jitong Chen +52
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific con…
SA-SOT: Speaker-Aware Serialized Output Training for Multi-Talker ASR
Zhiyun Fan, Linhao Dong, Jun Zhang +2
Multi-talker automatic speech recognition plays a crucial role in scenarios involving multi-party interactions, such as meetings and conversations. Due to its inherent complexity,…
Language-specific Acoustic Boundary Learning for Mandarin-English Code-switching Speech Recognition
Zhiyun Fan, Linhao Dong, Chen Shen +4
Code-switching speech recognition (CSSR) transcribes speech that switches between multiple languages or dialects within a single sentence. The main challenge in this task is that d…
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training
Linhao Dong, Zhecheng An, Peihao Wu +3
Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. I…
Token-level Speaker Change Detection Using Speaker Difference and Speech Content via Continuous Integrate-and-fire
Zhiyun Fan, Zhenlin Liang, Linhao Dong +6
In multi-talker scenarios such as meetings and conversations, speech processing systems are usually required to segment the audio and then transcribe each segmentation. These two s…
Sequence-level Speaker Change Detection with Difference-based Continuous Integrate-and-fire
Zhiyun Fan, Linhao Dong, Meng Cai +2
Speaker change detection is an important task in multi-party interactions such as meetings and conversations. In this paper, we address the speaker change detection task from the p…
Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection
Minglun Han, Linhao Dong, Zhenlin Liang +4
Nowadays, most methods in end-to-end contextual speech recognition bias the recognition process towards contextual knowledge. Since all-neural contextual biasing methods rely on ph…
CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition
Minglun Han, Linhao Dong, Shiyu Zhou +1
End-to-end (E2E) models have achieved promising results on multiple speech recognition benchmarks, and shown the potential to become the mainstream. However, the unified structure…
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition
Linhao Dong, Cheng Yi, Jianzong Wang +4
End-to-end models are gaining wider attention in the field of automatic speech recognition (ASR). One of their advantages is the simplicity of building that directly recognizes the…
CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition
Linhao Dong, Bo Xu
In this paper, we propose a novel soft and monotonic alignment mechanism used for sequence transduction. It is inspired by the integrate-and-fire model in spiking neural networks a…
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping
Linhao Dong, Feng Wang, Bo Xu
Self-attention network, an attention-based feedforward neural network, has recently shown the potential to replace recurrent neural networks (RNNs) in a variety of NLP tasks. Howev…
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin
Linhao Dong, Shiyu Zhou, Wei Chen +1
End-to-end models have been showing superiority in Automatic Speech Recognition (ASR). At the same time, the capacity of streaming recognition has become a growing requirement for…
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese
Shiyu Zhou, Linhao Dong, Shuang Xu +1
The choice of modeling units is critical to automatic speech recognition (ASR) tasks. Conventional ASR systems typically choose context-dependent states (CD-states) or context-depe…
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese
Shiyu Zhou, Linhao Dong, Shuang Xu +1
Sequence-to-sequence attention-based models have recently shown very promising results on automatic speech recognition (ASR) tasks, which integrate an acoustic, pronunciation and l…