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20242026
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cs.CL2026

On the Effect of Segmentation Width and Cluster Size on Speech Resynthesis and Continuation in Generative Spoken Language Models

Shunsuke Kando, Wataru Nakata, Shinnosuke Takamichi +1

Generative Spoken Language Modeling (GSLM) enables text-free speech modeling by training language models (LMs) using discrete speech representations instead of textual transcriptio…

cs.CL2026

Low-Latency Real-Time Audio Game Commentary System via LLM-Based Parallel Text Generation

Ryota Kawamatsu, Anum Afzal, Yuki Saito +5

We present a low-latency real-time audio game commentary system that generates spoken commentary directly from live gameplay video. In this end-to-end setting, a key bottleneck is…

cs.CL2026

J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling

Wataru Nakata, Kentaro Seki, Hitomi Yanaka +3

Spoken dialogue is essential for human-AI interactions, providing expressive capabilities beyond text. Developing effective spoken dialogue systems (SDSs) requires large-scale, hig…

cs.CL2026

Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches

Anum Afzal, Yuki Saito, Hiroya Takamura +5

Real-time video commentary generation provides textual descriptions of ongoing events in videos. It supports accessibility and engagement in domains such as sports, esports, and li…

cs.CL2025

Analysing the Language of Neural Audio Codecs

Joonyong Park, Shinnosuke Takamichi, David M. Chan +3

This study presents a comparative analysis of the statistical and linguistic properties of neural audio codecs (NACs). We investigate discrete speech tokens produced by various NAC…

cs.CL2025

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models

Shunsuke Kando, Yusuke Miyao, Shinnosuke Takamichi

The purpose of speech tokenization is to transform a speech signal into a sequence of discrete representations, serving as the foundation for speech language models (SLMs). While s…