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arXiv · 2502.02683

Streaming Speaker Change Detection and Gender Classification for Transducer-Based Multi-Talker Speech Translation

Peidong Wang·Naoyuki Kanda·Jian Xue·Jinyu Li·Xiaofei Wang·Aswin Shanmugam Subramanian·Junkun Chen·Sunit Sivasankaran·Xiong Xiao·Yong Zhao
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Abstract

Streaming multi-talker speech translation is a task that involves not only generating accurate and fluent translations with low latency but also recognizing when a speaker change occurs and what the speaker's gender is. Speaker change information can be used to create audio prompts for a zero-shot text-to-speech system, and gender can help to select speaker profiles in a conventional text-to-speech model. We propose to tackle streaming speaker change detection and gender classification by incorporating speaker embeddings into a transducer-based streaming end-to-end speech translation model. Our experiments demonstrate that the proposed methods can achieve high accuracy for both speaker change detection and gender classification.

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Peidong Wang, Naoyuki Kanda, Jian Xue, Jinyu Li, Xiaofei Wang, Aswin Shanmugam Subramanian, Junkun Chen, Sunit Sivasankaran, Xiong Xiao, Yong Zhao. 2025-02-04. Streaming Speaker Change Detection and Gender Classification for Transducer-Based Multi-Talker Speech Translation. https://arxiv.org/abs/2502.02683

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