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

Abstract

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.

Speech Enhancement Performance Based on the MANNER Network Using Feature Fusion

“…After compensating the level difference of the enhanced speech, the short-time envelope correlation coefficients of the two signals are calculated to obtain the STOI scores. The STOI score takes values in the interval [1,100].…”

“…ASR models are used to interconnect smart voice assistants such as Siri and Bixby on personal cell phones for network interconnection. An ASR model was also used to improve the recognition rate of the speech of non-standard cochlear implant users [1]. ASR models trained on standard speech datasets are not available for people using non-standard speech, and this study personalizes the pre-trained models.…”