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

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 p...

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Veröffentlicht in:International journal of speech technology Jg. 26; H. 4; S. 1031 - 1059
Hauptverfasser: Sheferaw, Gebremichael Kibret, Mwangi, Waweru, Kimwele, Michael, Mamuye, Adane
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.12.2023
Springer Nature B.V
Schlagworte:
Adaptive algorithms
Algorithms
Artificial Intelligence
Coding
Computer science
Criteria
Data quality
Engineering
Extraction
Intelligibility
Measurement
Networks
Neural networks
Nonlinearity
Polynomials
Predictions
Quality assessment
Recurrent neural networks
Selection criteria
Signal,Image and Speech Processing
Social Sciences
Speech
Speech compression
Systematic review
Topology
Waveforms
Waveform
Systematic review
Nonlinear
Speech coding
Neural network
Prediction
ISSN:1381-2416, 1572-8110
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Zusammenfassung: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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ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-023-10072-7