Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder
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| Publicado en: | EURASIP Journal on Audio, Speech, and Music Processing vol. 2024, no. 1 (Dec 2024), p. 6 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| Resumen: | Speech coding is a method to reduce the amount of data needs to represent speech signals by exploiting the statistical properties of the speech signal. Recently, in the speech coding process, a neural network prediction model has gained attention as the reconstruction process of a nonlinear and nonstationary speech signal. This study proposes a novel approach to improve speech coding performance by using a gated recurrent unit (GRU)-based adaptive differential pulse code modulation (ADPCM) system. This GRU predictor model is trained using a data set of speech samples from the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus actual sample and the ADPCM fixed-predictor output speech sample. Our contribution lies in the development of an algorithm for training the GRU predictive model that can improve its performance in speech coding prediction and a new offline trained predictive model for speech decoder. The results indicate that the proposed system significantly improves the accuracy of speech prediction, demonstrating its potential for speech prediction applications. Overall, this work presents a unique application of the GRU predictive model with ADPCM decoding in speech signal compression, providing a promising approach for future research in this field. |
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| ISSN: | 1687-4714 1687-4722 |
| DOI: | 10.1186/s13636-023-00325-3 |
| Fuente: | Advanced Technologies & Aerospace Database |