7 papers
Evaluating Japanese Dialect Robustness Across Speech and Text-based Large Language Models
Tomoya Mizumoto, Yusuke Fujita, Hao Shi +3
Dialogue systems based on large language models (LLMs) have advanced significantly in recent years. However, dialectal variation remains a major challenge, particularly for systems…
Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis
Lianbo Liu, Shiao Zhu, Kai Washizaki +10
While large language model (LLM)-based text-to-speech (TTS) systems have achieved high-quality speech synthesis, most existing systems focus on English and Chinese. Japanese, howev…
Training-Free Intelligibility-Guided Observation Addition for Noisy ASR
Haoyang Li, Changsong Liu, Wei Rao +3
Automatic speech recognition (ASR) degrades severely in noisy environments. Although speech enhancement (SE) front-ends effectively suppress background noise, they often introduce…
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…
Streaming Translation and Transcription Through Speech-to-Text Causal Alignment
Roman Koshkin, Jeon Haesung, Lianbo Liu +4
Simultaneous machine translation (SiMT) has traditionally relied on offline machine translation models coupled with human-engineered heuristics or learned policies. We propose Hika…
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…