One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition

• URL: http://arxiv.org/abs/2310.01688v1
• Date: Mon, 2 Oct 2023 23:03:30 GMT
• Title: One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition
• Authors: Samuele Cornell, Jee-weon Jung, Shinji Watanabe, Stefano Squartini
• Abstract summary: This paper presents a novel framework for joint speaker diarization and automatic speech recognition. The framework, named SLIDAR, can process arbitrary length inputs and can handle any number of speakers. Experiments performed on monaural recordings from the AMI corpus confirm the effectiveness of the method in both close-talk and far-field speech scenarios.
• Score: 50.055765860343286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel framework for joint speaker diarization (SD) and automatic speech recognition (ASR), named SLIDAR (sliding-window diarization-augmented recognition). SLIDAR can process arbitrary length inputs and can handle any number of speakers, effectively solving ``who spoke what, when'' concurrently. SLIDAR leverages a sliding window approach and consists of an end-to-end diarization-augmented speech transcription (E2E DAST) model which provides, locally, for each window: transcripts, diarization and speaker embeddings. The E2E DAST model is based on an encoder-decoder architecture and leverages recent techniques such as serialized output training and ``Whisper-style" prompting. The local outputs are then combined to get the final SD+ASR result by clustering the speaker embeddings to get global speaker identities. Experiments performed on monaural recordings from the AMI corpus confirm the effectiveness of the method in both close-talk and far-field speech scenarios.

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