Gebremichael Kibret Sheferaw

Speech coding is important for effective storage and transmission of audio signals. However, current Interactive Multimedia Association Adaptive Differential Pulse Code Modulation (IMA-ADPCM) speech coding techniques that use a fixed predictor have an impact on the encoding of dynamic and non-stationary speech signals. The limitation of the fixed predictor in IMA-ADPCM speech coding is the motivation for this study. Our goal is to improve the fixed predictor by integrating a GRU predictor that can adapt to and make better predictions of dynamic speech signals. We evaluated the performance of the IMA-ADPCM encoding baseline and the GRU predictor embedded with the IMA-ADPCM codec algorithm. The proposed pre-trained GRU predictor based encoding system outperformed the maximum Signal-to-Noise Ratio (SNR) (43.2 dB and MOS scores 3.8 to 4.3) of 5.0, and our results demonstrated considerable improvements in audio quality. The main contribution of this study is the development of a GRU Predictor that integrates IMA-ADPCM coding algorithms according to the IMA-ADPCM output speech sample and the actual PCM speech sample dataset required. By integrating the GRU predictor model in accordance with these data samples, the newly designed algorithm significantly improved the quality of the IMA-ADPCM speech codec.

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.