Assessment of Syllable Intelligibility Based on Convolutional Neural Networks for Speech Rehabilitation After Speech Organs Surgical Interventions
| Autor: | Mikhail Nemirovich-Danchenko, Svetlana Pekarskikh, Dariya Novokhrestova, Evgeny L. Choynzonov, Lidiya N. Balatskaya, Alexander Alexandrovich Shelupanov, Evgeny Kostuchenko |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Rehabilitation Artificial neural network Computer science medicine.medical_treatment Speech recognition media_common.quotation_subject Speech organ 02 engineering and technology Speech rehabilitation Intelligibility (communication) Convolutional neural network 020901 industrial engineering & automation Perception 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Mel-frequency cepstrum media_common |
| Zdroj: | Speech and Computer ISBN: 9783030260606 SPECOM |
| DOI: | 10.1007/978-3-030-26061-3_37 |
| Popis: | Head and neck cancer patients often have side effects that make speaking and communicating more difficult. During the speech therapy the approach of perceptual evaluation of voice quality is widely used. First of all, this approach is subjective as it depends on the listener’s perception. Secondly, the approach requires the patient to visit a hospital regularly. The present study is aimed to develop the automatic assessment of pathological speech based on convolutional neural networks to give more objective feedback of the speech quality. The structure of the neural network has been selected based on experimental results. The neural network is trained and validated on the dataset of phonemes which are represented as Mel-frequency cepstral coefficients. The neural network is tested on the syllable dataset. Recognition of the phoneme content of the syllable pronounced by a patient allows to evaluate the progress of the rehabilitation. A conclusion about the applicability of this approach and recommendations for the further improvement of its performance were made. |
| Databáze: | OpenAIRE |
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