End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model Pretraining

• URL: http://arxiv.org/abs/2309.04516v1
• Date: Fri, 8 Sep 2023 17:12:14 GMT
• Title: End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model Pretraining
• Authors: Saksham Bassi, Giulio Duregon, Siddhartha Jalagam, David Roth
• Abstract summary: We revisit the performance comparison between two-stage and end-to-end model. We find that audio based language models pretrained using weak self-supervised objectives match or exceed the performance of similarly trained two-stage models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The SOTA in transcription of disfluent and conversational speech has in recent years favored two-stage models, with separate transcription and cleaning stages. We believe that previous attempts at end-to-end disfluency removal have fallen short because of the representational advantage that large-scale language model pretraining has given to lexical models. Until recently, the high dimensionality and limited availability of large audio datasets inhibited the development of large-scale self-supervised pretraining objectives for learning effective audio representations, giving a relative advantage to the two-stage approach, which utilises pretrained representations for lexical tokens. In light of recent successes in large scale audio pretraining, we revisit the performance comparison between two-stage and end-to-end model and find that audio based language models pretrained using weak self-supervised objectives match or exceed the performance of similarly trained two-stage models, and further, that the choice of pretraining objective substantially effects a model's ability to be adapted to the disfluency removal task.

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