Simulating Speech Coders Using Neural Networks  

Oleh:

Al-Akaidi, Marwan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Simulation vol. 71 no. 1 (Jan. 1995), page 23-30.
Topik: Digital signal processing; speech coders; neural networks
Fulltext: 23.pdf (634.64KB)

Isi artikel

Speech coding algorithms are developed and optimised to satisfy many applications’ specific requirements. By using these requirements to analyse different types of speech coders operating at various bit rates, the most efficient speech coding scheme for a particular application can be selected. Analysing speech coders is a time-consuming, expensive process because many of the tests are based on human perception of the reconstructed speech. An efficient, cost-effective method of performing this analysis is to use a neural network. After a neural network is trained using characteristic parameters of the reconstructed speech signal, it can be used to classify a speech coder’s performance in terms of its application requirements. This greatly simplifies the process of selecting a speech coder for a particular application. This paper shows how a neural network can be used to identify the speech quality and signal-tonoise ratio of a speech waveform produced by a speech coder, and how it can be used to determine if a speech coder is suitable for a particular application

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