1 citations · 1 across the 6 of their papers we have counts for
6 papers
Speech-Worthy Alignment for Japanese SpeechLLMs via Direct Preference Optimization
Mengjie Zhao, Lianbo Liu, Yusuke Fujita +4
SpeechLLMs typically combine ASR-trained encoders with text-based LLM backbones, leading them to inherit written-style output patterns unsuitable for text-to-speech synthesis. This…
Distilling LLM Semantic Priors into Encoder-Only Multi-Talker ASR with Talker-Count Routing
Hao Shi, Yusuke Fujita, Roman Koshkin +4
Large language models (LLMs) provide strong semantic priors that can improve multi-talker automatic speech recognition (MT-ASR), but using an LLM as an autoregressive decoder is co…
DuplexCascade: Full-Duplex Speech-to-Speech Dialogue with VAD-Free Cascaded ASR-LLM-TTS Pipeline and Micro-Turn Optimization
Jianing Yang, Yusuke Fujita, Yui Sudo
Spoken dialog systems with cascaded ASR-LLM-TTS modules retain strong LLM intelligence, but VAD segmentation often forces half-duplex turns and brittle control. On the other hand,…
Serialized Output Prompting for Large Language Model-based Multi-Talker Speech Recognition
Hao Shi, Yusuke Fujita, Tomoya Mizumoto +3
Prompts are crucial for task definition and for improving the performance of large language models (LLM)-based systems. However, existing LLM-based multi-talker (MT) automatic spee…
AC/DC: LLM-based Audio Comprehension via Dialogue Continuation
Yusuke Fujita, Tomoya Mizumoto, Atsushi Kojima +2
We propose an instruction-following audio comprehension model that leverages the dialogue continuation ability of large language models (LLMs). Instead of directly generating targe…
OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary
Yui Sudo, Yusuke Fujita, Atsushi Kojima +2
Speech foundation models (SFMs), such as Open Whisper-Style Speech Models (OWSM), are trained on massive datasets to achieve accurate automatic speech recognition. However, even SF…