Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training

• URL: http://arxiv.org/abs/2203.00648v3
• Date: Sun, 11 Jun 2023 22:41:52 GMT
• Title: Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training
• Authors: Ramon Sanabria, Wei-Ning Hsu, Alexei Baevski, Michael Auli
• Abstract summary: We show that phonetic domain factors play an important role during pre-training while grammatical and syntactic factors are far less important. This is the first study to better understand the domain characteristics of pre-trained sets in self-supervised pre-training for speech.
• Score: 60.825471653739555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human speech data comprises a rich set of domain factors such as accent, syntactic and semantic variety, or acoustic environment. Previous work explores the effect of domain mismatch in automatic speech recognition between pre-training and fine-tuning as a whole but does not dissect the contribution of individual factors. In this paper, we present a controlled study to better understand the effect of such factors on the performance of pre-trained representations on automatic speech recognition. To do so, we pre-train models either on modified natural speech or synthesized audio, with a single domain factor modified, and then measure performance after fine-tuning. Results show that phonetic domain factors play an important role during pre-training while grammatical and syntactic factors are far less important. To our knowledge, this is the first study to better understand the domain characteristics of pre-trained sets in self-supervised pre-training for speech.

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