An Effective Learning Method for Automatic Speech Recognition in Korean CI Patients’ Speech

The automatic speech recognition (ASR) model usually requires a large amount of training data to provide better results compared with the ASR models trained with a small amount of training data. It is difficult to apply the ASR model to non-standard speech such as that of cochlear implant (CI) patie...

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Bibliographic Details
Main Authors: Jiho Jeong, S. I. M. M. Raton Mondol, Yeon Wook Kim, Sangmin Lee
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/10/7/807
Description
Summary:The automatic speech recognition (ASR) model usually requires a large amount of training data to provide better results compared with the ASR models trained with a small amount of training data. It is difficult to apply the ASR model to non-standard speech such as that of cochlear implant (CI) patients, owing to privacy concerns or difficulty of access. In this paper, an effective finetuning and augmentation ASR model is proposed. Experiments compare the character error rate (CER) after training the ASR model with the basic and the proposed method. The proposed method achieved a CER of 36.03% on the CI patient’s speech test dataset using only 2 h and 30 min of training data, which is a 62% improvement over the basic method.
ISSN:2079-9292