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

• URL: http://arxiv.org/abs/2502.02683v1
• Date: Tue, 04 Feb 2025 19:50:15 GMT
• Title: Streaming Speaker Change Detection and Gender Classification for Transducer-Based Multi-Talker Speech Translation
• Authors: Peidong Wang, Naoyuki Kanda, Jian Xue, Jinyu Li, Xiaofei Wang, Aswin Shanmugam Subramanian, Junkun Chen, Sunit Sivasankaran, Xiong Xiao, Yong Zhao,
• Abstract summary: 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.
• Score: 45.51065693072839
• License:
• 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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