How does cross-lingual self-supervised pre-training impact convergence speed and word error rate on code-switc

Abstract

Abstract: Self-supervised pre-training could effectively improve the performance of low-resource automatic speech recognition (ASR). However, existing self-supervised pre-training are task-agnostic, i.e., could be applied to various downstream tasks. Although it enlarges the scope of its application, the capacity of the pre-trained model is not fully utilized for the ASR task, and the learned representations may not be optimal for ASR. In this work, in order to build a better pre-trained model for low-resource ASR, we propose a pre-training approach called wav2vec-S, where we use task-specific semi-supe

Research Question

How does cross-lingual self-supervised pre-training impact convergence speed and word error rate on code-switched ASR benchmarks compared to monolingual initialization?

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