Knowledge Transferability Between the Speech Data of Persons With Dysarthria Speaking Different Languages for Dysarthric Speech Recognition

• Published in: IEEE access Vol. 7; pp. 164320 - 164326
• Main Authors: Takashima, Yuki, Takashima, Ryoichi, Takiguchi, Tetsuya, Ariki, Yasuo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustics; Assistive technology; Data models; Decoding; deep learning; dysarthria; end-to-end model; Hidden Markov models; Knowledge management; knowledge transfer; Languages; Machine learning; multilingual; Speech; speech processing; Speech recognition; Voice recognition
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2951856

In this paper, we present an end-to-end speech recognition system for Japanese persons with articulation disorders resulting from athetoid cerebral palsy. Because their utterance is often unstable or unclear, speech recognition systems struggle to recognize their speech. Recent deep learning-based approaches have exhibited promising performance. However, these approaches require a large amount of training data, and it is difficult to collect sufficient data from such dysarthric people. This paper proposes a transfer learning method that transfers two types of knowledge corresponding to the different datasets: the language-dependent (phonetic and linguistic) characteristic of unimpaired speech and the language-independent characteristic of dysarthric speech. The former is obtained from Japanese non-dysarthric speech data, and the latter is obtained from non-Japanese dysarthric speech data. In the proposed method, we pre-train a model using Japanese non-dysarthric speech and non-Japanese dysarthric speech, and thereafter, we fine-tune the model using the target Japanese dysarthric speech. To handle the speech data of the two different languages in one model, we employ language-specific decoder modules. Experimental results indicate that our proposed approach can significantly improve speech recognition performance compared with other approaches that do not use additional speech data.