Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition

Asahi Sakuma; Hiroaki Sato; Ryuga Sugano; Tadashi Kumano; Yoshihiko Kawai; Tetsuji Ogawa

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arXiv · 2506.07515

Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition

Asahi Sakuma·Hiroaki Sato·Ryuga Sugano·Tadashi Kumano·Yoshihiko Kawai·Tetsuji Ogawa

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Abstract

This paper presents a novel framework for multi-talker automatic speech recognition without the need for auxiliary information. Serialized Output Training (SOT), a widely used approach, suffers from recognition errors due to speaker assignment failures. Although incorporating auxiliary information, such as token-level timestamps, can improve recognition accuracy, extracting such information from natural conversational speech remains challenging. To address this limitation, we propose Speaker-Distinguishable CTC (SD-CTC), an extension of CTC that jointly assigns a token and its corresponding speaker label to each frame. We further integrate SD-CTC into the SOT framework, enabling the SOT model to learn speaker distinction using only overlapping speech and transcriptions. Experimental comparisons show that multi-task learning with SD-CTC and SOT reduces the error rate of the SOT model by 26% and achieves performance comparable to state-of-the-art methods relying on auxiliary information.

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Asahi Sakuma, Hiroaki Sato, Ryuga Sugano, Tadashi Kumano, Yoshihiko Kawai, Tetsuji Ogawa. 2025-06-09. Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition. https://arxiv.org/abs/2506.07515

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