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Waveform based speech coding using nonlinear predictive techniques: a systematic review

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Abstract

Speech coding is a technique that compresses speech signals into a smaller digital form, making it easier to transmit or store, while still maintaining the quality and intelligibility of the speech. The review aimed to identify and analyses the most effective waveform-based nonlinear speech coding prediction techniques, including the use of neural networks and polynomial filters. The study analyzed 29 publications from 2000 to 2023 and found that neural network-based models are widely used for speech compression, with RNN topologies being favored due to their ability to introduce nonlinearity and nonstationary. While nonlinear adaptive speech prediction techniques have been explored for speech coding, further research is needed to optimize the adaptive algorithms used in these models. The review also identified a need for future research to address quality performance and computational cost, and suggested further exploration of RNN predictor models. The methodology used in this study involved a computer science approach that follows three main phases: planning, conducting, and reporting. Six different stages were followed, including determining research questions, defining research approach, study selection criteria, quality measurement criteria, data extraction strategy, and synthesizing extracted data. Overall, this study highlights the need for continued research in the development and improvement of neural network-based speech compression models.

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Data availability

The findings of this systematic literature review are based on previously published studies. All necessary references, including authors, titles, publication years, and sources, have been provided in the reference section. No primary data were generated during this study. Readers can access the data by referring to the original publications cited in this manuscript. Contact information for corresponding authors can be found within the respective publications. No additional datasets or supplementary materials were used. The methods, including study selection and data synthesis, are described in the Methods section. The search strategy employed and databases used are also provided. We acknowledge the authors of the included studies for their contributions to the existing literature.

Abbreviations

ADC:

Analog to digital converter

ADPCM:

Adaptive differential pulse code modulation

APCM:

Algebraic pulse code modulation

ATC:

Adaptive transform coding

BPTT:

Backpropagation through time

CELP:

Code excited linear prediction

CNN:

Convolutional neural network

CWT:

Continuous wavelet transform

DCT:

Discrete Cosine transform

DM:

Delta modulation

DPCM:

Differential pulse code modulation

DWT:

Discrete wavelet transform

FFT:

Fast Fourier transform

GRUs:

Gated recurrent units

ITU-T:

International telecommunication union telecommunication

LMS:

Least Mean squares

LPC:

Linear Predictive coding

LSTM:

Long Short-term memory

MDCT:

Modified discrete cosine transform

MELP:

Mixed excitation linear prediction

MLP:

Multilayer perceptron

MOS:

Mean opinion score

MSE:

Mean squared error

IMA:

Interactive Multimedia Association

SNR:

Signal-to-noise ratio

SEGSNR:

Segmental signal-to-noise ratio

PCM:

Pulse-code modulation

POLQA:

Perceptual Objective Listening Quality Assessment

TIMIT:

Texas Instruments Misspoken Telephone Corpus

NN:

Neural network

PCM:

Pulse Code Modulation

RELP:

Residual Excited Linear Prediction

RLS:

Recursive Least Squares

RNN:

Recurrent Neural Network

RTRL:

Real-time recurrent learning

SBC:

Sub-Band Coding

SELP:

Stochastic excitation linear prediction

SLR:

System literature review

VFC:

Variance fractal compression

VoIP:

Voice over internet protocol

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Acknowledgements

I would like to express my gratitude to the German Academic Exchange Service (DAAD) for providing funding for my PhD studies, including support for tuition fees, research expenses, and a stipend for living expenses. I am also thankful to Jomo Kenyatta University of Agriculture and Technology (JKUAT) for hosting me as a PhD student and providing invaluable academic resources and support.

Funding

This research was supported by a scholarship from DAAD, which provided funding for tuition fees, research expenses, and a stipend for the author during my PhD studies at Jomo Kenyatta University of Agriculture and Technology (JKUAT), Kenya.

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Authors and Affiliations

  1. School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Juja, Nairobi, 62000, Nairobi, Kenya

    Gebremichael Kibret Sheferaw, Waweru Mwangi & Michael Kimwele

  2. School of Information Technology and Engineering, Addis Ababa University Institute of Technology, 5Kilo, Addis-Ababa, Addis-Ababa, Ethiopia

    Adane Mamuye

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  1. Gebremichael Kibret Sheferaw
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Appendix

Appendix

See Table 9 and 10.

Table 9 List of publication and source information of selected papers
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Table 10 The studies of information extraction the studies of information extraction related to the research questions
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Sheferaw, G.K., Mwangi, W., Kimwele, M. et al. Waveform based speech coding using nonlinear predictive techniques: a systematic review. Int J Speech Technol 26, 1031–1059 (2023). https://doi.org/10.1007/s10772-023-10072-7

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