Investigating Pre-trained Audio Encoders in the Low-Resource Condition

• URL: http://arxiv.org/abs/2305.17733v1
• Date: Sun, 28 May 2023 14:15:19 GMT
• Title: Investigating Pre-trained Audio Encoders in the Low-Resource Condition
• Authors: Hao Yang, Jinming Zhao, Gholamreza Haffari, Ehsan Shareghi
• Abstract summary: We conduct a comprehensive set of experiments using a representative set of 3 state-of-the-art encoders (Wav2vec2, WavLM, Whisper) in the low-resource setting. We provide various quantitative and qualitative analyses on task performance, convergence speed, and representational properties of the encoders.
• Score: 66.92823764664206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained speech encoders have been central to pushing state-of-the-art results across various speech understanding and generation tasks. Nonetheless, the capabilities of these encoders in low-resource settings are yet to be thoroughly explored. To address this, we conduct a comprehensive set of experiments using a representative set of 3 state-of-the-art encoders (Wav2vec2, WavLM, Whisper) in the low-resource setting across 7 speech understanding and generation tasks. We provide various quantitative and qualitative analyses on task performance, convergence speed, and representational properties of the encoders. We observe a connection between the pre-training protocols of these encoders and the way in which they capture information in their internal layers. In particular, we observe the Whisper encoder exhibits the greatest low-resource capabilities on content-driven tasks in terms of performance and convergence speed.

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