Dyn-ASR: Compact, Multilingual Speech Recognition via Spoken Language and Accent Identification

• URL: http://arxiv.org/abs/2108.02034v1
• Date: Wed, 4 Aug 2021 12:59:53 GMT
• Title: Dyn-ASR: Compact, Multilingual Speech Recognition via Spoken Language and Accent Identification
• Authors: Sangeeta Ghangam, Daniel Whitenack, Joshua Nemecek
• Abstract summary: We propose a new approach to enable multilingual speech recognition on edge devices. This approach uses both language identification and accent identification to select one of multiple monolingual ASR models on-the-fly. Initial results for both recognition performance and resource usage are promising with our approach using less than 1/12th of the memory consumed by other solutions.
• Score: 0.013428344011390777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Running automatic speech recognition (ASR) on edge devices is non-trivial due to resource constraints, especially in scenarios that require supporting multiple languages. We propose a new approach to enable multilingual speech recognition on edge devices. This approach uses both language identification and accent identification to select one of multiple monolingual ASR models on-the-fly, each fine-tuned for a particular accent. Initial results for both recognition performance and resource usage are promising with our approach using less than 1/12th of the memory consumed by other solutions.

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