META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR

Jinhan Wang; Weiqing Wang; Kunal Dhawan; Taejin Park; Myungjong Kim; Ivan Medennikov; He Huang; Nithin Koluguri; Jagadeesh Balam; Boris Ginsburg

arXiv · 2409.12352

META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR

Jinhan Wang·Weiqing Wang·Kunal Dhawan·Taejin Park·Myungjong Kim·Ivan Medennikov·He Huang·Nithin Koluguri·Jagadeesh Balam·Boris Ginsburg

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Abstract

We propose a novel end-to-end multi-talker automatic speech recognition (ASR) framework that enables both multi-speaker (MS) ASR and target-speaker (TS) ASR. Our proposed model is trained in a fully end-to-end manner, incorporating speaker supervision from a pre-trained speaker diarization module. We introduce an intuitive yet effective method for masking ASR encoder activations using output from the speaker supervision module, a technique we term Meta-Cat (meta-information concatenation), that can be applied to both MS-ASR and TS-ASR. Our results demonstrate that the proposed architecture achieves competitive performance in both MS-ASR and TS-ASR tasks, without the need for traditional methods, such as neural mask estimation or masking at the audio or feature level. Furthermore, we demonstrate a glimpse of a unified dual-task model which can efficiently handle both MS-ASR and TS-ASR tasks. Thus, this work illustrates that a robust end-to-end multi-talker ASR framework can be implemented with a streamlined architecture, obviating the need for the complex speaker filtering mechanisms employed in previous studies.

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BibTeX RIS

Jinhan Wang, Weiqing Wang, Kunal Dhawan, Taejin Park, Myungjong Kim, Ivan Medennikov, He Huang, Nithin Koluguri, Jagadeesh Balam, Boris Ginsburg. 2024-09-18. META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR. https://arxiv.org/abs/2409.12352

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