8 citations · 8 across the 7 of their papers we have counts for
7 papers
Compress the Cache, Not the Speech Embedding: KV Compression for Efficient Speech LLMs
Ke-Han Lu, Keqi Deng, Ruchao Fan +2
Speech large language models (Speech LLMs) typically encode speech into sequences far longer than text, creating a major efficiency bottleneck during autoregressive decoding. A com…
Rethinking Speech-LLM Integration for ASR: Effective Joint Speech-Text Training by Interleaving
Ruchao Fan, Yiming Wang, Rui Zhao +10
Speech-LLM integration has shown promising results by leveraging extensive textual pretraining, yet its specific benefits for automatic speech recognition (ASR) remain unclear. We…
Interleaved Speech-Text Language Models for Simple Streaming Text-to-Speech Synthesis
Yifan Yang, Shujie Liu, Jinyu Li +10
This paper introduces Interleaved Speech-Text Language Model (IST-LM) for zero-shot streaming Text-to-Speech (TTS). Unlike many previous approaches, IST-LM is directly trained on i…
AlignFormer: Modality Matching Can Achieve Better Zero-shot Instruction-Following Speech-LLM
Ruchao Fan, Bo Ren, Yuxuan Hu +3
Integrating speech into LLM (speech-LLM) has gaining increased attention recently. The mainstream solution is to connect a well-trained speech encoder and LLM with a neural adapter…
CTC-GMM: CTC guided modality matching for fast and accurate streaming speech translation
Rui Zhao, Jinyu Li, Ruchao Fan +1
Models for streaming speech translation (ST) can achieve high accuracy and low latency if they're developed with vast amounts of paired audio in the source language and written tex…
Advanced Long-Content Speech Recognition With Factorized Neural Transducer
Xun Gong, Yu Wu, Jinyu Li +4
In this paper, we propose two novel approaches, which integrate long-content information into the factorized neural transducer (FNT) based architecture in both non-streaming (refer…
t-SOT FNT: Streaming Multi-talker ASR with Text-only Domain Adaptation Capability
Jian Wu, Naoyuki Kanda, Takuya Yoshioka +3
Token-level serialized output training (t-SOT) was recently proposed to address the challenge of streaming multi-talker automatic speech recognition (ASR). T-SOT effectively handle…