Optimizing Medical Personnel Speech Recognition Models Using Speech Synthesis and Reinforcement Learning

Andrzej Czyżewski

doi:10.1121/10.0023271

Optimizing Medical Personnel Speech Recognition Models Using Speech Synthesis and Reinforcement Learning

Abstract

Text-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons; TTS can help expand the data set. Speech can be synthesized by mimicking different accents, dialects, and speaking styles that may occur in a medical language. Reinforcement Learning (RL), in the context of ASR, can be used to optimize a model based on specific goals. A model can be trained to minimize errors in speech-to-text transcription, especially for technical medical terminology. In this case, the "reward" to the RL model can be negatively proportional to the number of transcription errors. The paper presents a method and experimental study from which it is concluded that the combination of TTS and RL can enable the creation of a speech recognition model that is better suited to the specific needs of medical personnel, helping to expand the training data and optimize the model to minimize transcription errors. The learning process used reward functions based on Mean Opinion Score (MOS), a subjective metric for assessing speech quality, and Word Error Rate (WER), which evaluates the quality of speech-to-text transcription.

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Author (1)

Andrzej Czyżewski prof. dr hab. inż.

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Publication version: Accepted or Published Version
DOI:: Digital Object Identifier (open in new tab) 10.1121/10.0023271
License: Copyright (2023 Acoustical Society of America)

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Category:

Articles

Type:

artykuły w czasopismach

Published in:

Journal of the Acoustical Society of America no. 154, pages A202 - A203,
ISSN: 0001-4966

Language:

English

Publication year:

2023

Bibliographic description:

Czyżewski A.: Optimizing Medical Personnel Speech Recognition Models Using Speech Synthesis and Reinforcement Learning// Journal of the Acoustical Society of America -Vol. 154,iss. 4suppl (2023), s.A202-A203

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