13 citations · 23 across the 7 of their papers we have counts for
7 papers
Fun-ASR Technical Report
Keyu An, Yanni Chen, Zhigao Chen +35
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep in…
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction
Qian Chen, Yafeng Chen, Yanni Chen +33
Recent advancements in large language models (LLMs) and multimodal speech-text models have laid the groundwork for seamless voice interactions, enabling real-time, natural, and hum…
Accurate and Reliable Confidence Estimation Based on Non-Autoregressive End-to-End Speech Recognition System
Xian Shi, Haoneng Luo, Zhifu Gao +2
Estimating confidence scores for recognition results is a classic task in ASR field and of vital importance for kinds of downstream tasks and training strategies. Previous end-to-e…
FunASR: A Fundamental End-to-End Speech Recognition Toolkit
Zhifu Gao, Zerui Li, Jiaming Wang +8
This paper introduces FunASR, an open-source speech recognition toolkit designed to bridge the gap between academic research and industrial applications. FunASR offers models train…
Boundary and Context Aware Training for CIF-based Non-Autoregressive End-to-end ASR
Fan Yu, Haoneng Luo, Pengcheng Guo +6
Continuous integrate-and-fire (CIF) based models, which use a soft and monotonic alignment mechanism, have been well applied in non-autoregressive (NAR) speech recognition with com…
Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition
Shiliang Zhang, Zhifu Gao, Haoneng Luo +4
Recently, streaming end-to-end automatic speech recognition (E2E-ASR) has gained more and more attention. Many efforts have been paid to turn the non-streaming attention-based E2E-…
Simplified Self-Attention for Transformer-based End-to-End Speech Recognition
Haoneng Luo, Shiliang Zhang, Ming Lei +1
Transformer models have been introduced into end-to-end speech recognition with state-of-the-art performance on various tasks owing to their superiority in modeling long-term depen…