Pushing the performances of ASR models on English and Spanish accents

• URL: http://arxiv.org/abs/2212.12048v1
• Date: Thu, 22 Dec 2022 21:48:29 GMT
• Title: Pushing the performances of ASR models on English and Spanish accents
• Authors: Pooja Chitkara, Morgane Riviere, Jade Copet, Frank Zhang, Yatharth Saraf
• Abstract summary: We show how pre-trained embeddings and auxiliary classification losses can improve the performance of ASR systems. We are looking for upgrades as universal as possible and therefore we will explore their impact on several models architectures and several languages.
• Score: 13.031634839137773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech to text models tend to be trained and evaluated against a single target accent. This is especially true for English for which native speakers from the United States became the main benchmark. In this work, we are going to show how two simple methods: pre-trained embeddings and auxiliary classification losses can improve the performance of ASR systems. We are looking for upgrades as universal as possible and therefore we will explore their impact on several models architectures and several languages.

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