福田, 芽衣子 Tokushima University
Nishizaki, Hiromitsu University of Yamanashi
Horii, Koharu Toyohashi University of Technology
Iribe, Yurie Aichi Prefectural University
Yamamoto, Kazumasa Chubu University
The development of accessible speech recognition technology will allow the elderly to more easily access electronically stored information. However, the necessary level of recognition accuracy for elderly speech has not yet been achieved using conventional speech recognition systems, due to the unique features of the speech of elderly people. To address this problem, we have created a new speech corpus named EARS (Elderly Adults Read Speech), consisting of the recorded read speech of 123 super-elderly Japanese people (average age: 83.1), as a resource for training automated speech recognition models for the elderly. In this study, we investigated the acoustic features of super-elderly Japanese speech using our new speech corpus. In comparison to the speech of less elderly Japanese speakers, we observed a slower speech rate and extended vowel duration for both genders, a slight increase in fundamental frequency for males, and a slight decrease in fundamental frequency for females. To demonstrate the efficacy of our corpus, we also conducted speech recognition experiments using two different acoustic models (DNN-HMM and transformer-based), trained with a combination of data from our corpus and speech data from three conventional Japanese speech corpora. When using the DNN-HMM trained with EARS and speech data from existing corpora, the character error rate (CER) was reduced by 7.8% (to just over 9%), compared to a CER of 16.9% when using only the baseline training corpora. We also investigated the effect of training the models with various amounts of EARS data, using a simple data expansion method. The acoustic models were also trained for various numbers of epochs without any modifications. When using the Transformer-based end-to-end speech recognizer, the character error rate was reduced by 3.0% (to 11.4%) by using a doubled EARS corpus with the baseline data for training, compared to a CER of 13.4% when only data from the baseline training corpora were used.
Computer Speech & Language
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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