DNN-based Voice Conversion with Auxiliary Phonemic Information to Improve Intelligibility of Glossectomy Patients' Speech

• Murakami, Hiroki

抄録

In this paper, we propose using phonemic information in addition to acoustic features to improve the intelligibility of speech uttered by patients with articulation disorders caused by a wide glossectomy. Our previous studies showed that voice conversion algorithm improves the quality of glossectomy patients' speech. However, losses in acoustic features of glossectomy patients' speech are so large that the quality of the reconstructed speech is low. To solve this problem, we explored potentials of several additional information to improve speech intelligibility. One of the candidates is phonemic information, more specifically Phoneme Labels as Auxiliary input (PLA). To combine both acoustic features and PLA, we employed a DNN-based algorithm. PLA is represented by a kind of one-of-k vector, i.e., PLA has a weight value (<; 1.0) that gradually changes in time axis, whereas one-of-k has a binary value (0 or 1). The results showed that the proposed algorithm reduced the mel-frequency cepstral distortion for all phonemes, and almost always improved intelligibility. Notably, the intelligibility was largely improved in phonemes /s/ and /z/, mainly because the tongue is used to sustain constriction to produces these phonemes. This indicates that PLA works well to compensate the lack of a tongue.

収録刊行物

• Proceedings of APSIPA Annual Summit and Conference

  Proceedings of APSIPA Annual Summit and Conference 2019 138-142, 2019-11

  IEEE

詳細情報

• CRID: 1050574047101997824
• NII論文ID: 120006881465
• ISSN: 2640009X
• Web Site: https://ousar.lib.okayama-u.ac.jp/57782
• 本文言語コード: en
• 資料種別: conference paper
• データソース種別:
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  • CiNii Articles