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cs.CL2026
BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR
Chihiro Taguchi, Yotaro Kubo, Rujikorn Charakorn
Transcribing domain-specific entities and rare proper nouns remains a major challenge in automatic speech recognition (ASR). In this paper, we propose BaLEEN (Biasing with Latent E…
cs.CL2026
Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoring
Hikaru Asano, Yotaro Kubo, So Kuroki
Accurate and efficient pronunciation transcription is essential for preparing text-to-speech training data at scale. Existing approaches have different limitations: grapheme-to-pro…
cs.CL2026
Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance
Manato Yaguchi, Yotaro Kubo, Hikaru Asano +1
Tandem speech-to-speech architectures couple a responsive speech frontend with an asynchronous text backend. In KAME, a large language model (LLM) serves as the backend, supplying…