Application of Knowledge Distillation to Multi-task Speech Representation Learning
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Model architectures such as wav2vec 2.0 and HuBERT have been proposed to learn speech representations from audio waveforms in a self-supervised manner. When these models are combined with downstream tasks such as speech recognition, they have been shown to provide state-of-the-art performance. However, these models use a large number of parameters, the smallest version of which has about 95 million parameters. This constitutes a challenge for edge AI device deployments. In this paper, we use knowledge distillation to reduce the original model size by about 75 Moreover, we use wav2vec 2.0 and HuBERT models for distillation and present a comprehensive performance analysis through our experiments where we fine-tune the distilled models on single task and multi-task frameworks separately. In particular, our experiments show that fine-tuning the distilled models on keyword spotting and speaker verification tasks result in only 0.1 and 0.9
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