6 papers
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…
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…
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…
Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
Mengge Xue, Zhenyu Hu, Liqun Liu +5
Multiple-Choice Questions (MCQs) constitute a critical area of research in the study of Large Language Models (LLMs). Previous works have investigated the selection bias problem in…
Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding
Kuo Liao, Shuang Li, Meng Zhao +5
Recent strides in large language models (LLMs) have yielded remarkable performance, leveraging reinforcement learning from human feedback (RLHF) to significantly enhance generation…