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IMPROVE THE PERFORMANCE OF VOICE-EXCITED LPC VOCODER IN SPEECH CODING APPLICATION | ||
| Al-Qadisiyah Journal for Engineering Sciences | ||
| Article 1, Volume 8, Issue 4, 2015, Pages 558-568 PDF (0 K) | ||
| Authors | ||
| Ahmed Hussein Shatti Alisawi; Awwab Qasim Jumaah Althahab | ||
| Abstract | ||
| One of the fundamental problems in the area of digital speech processing is a speech coding that has been studied for years. Speech coding simply transforms the speech signals as fewer numbers of binary digits as possible, which can be then transmitted through channels or stored in memory devices. Due to the fact that the bandwidth of the channels is not unlimited, speech compression is needed to let more space bandwidth; thereby more speech coded signals can be sent over same channel bandwidth. Linear Predictive Coding (LPC) that is based on linear prediction (LP) model, which is a method to represent and analyze human speech, is one of the most common speech coding techniques. It is used in compression the digital speech signals, resulting low bit rate. This method has become the dominant technique for determine the fundamental speech parameters such as pitch, formants, spectra, vocal tract area functions. However, the weakness of LPC is in estimating the fundamental speech parameters causes poor voice quality and performance. The aim of this paper is to build a system with precise detection of speech parameters for encoding a better speech quality at low bit rate. This can be done through proposing a modified version to the voiceexcited LPC vocoder based on Discrete Cosine Transform (DCT) and quantization of residual error while retaining low bit rate; hence conserve the bandwidth. Segmental power signal to noise ratio (SEGPSNR) and mean square error (MSE) as an objective measure for speech signal quality are implemented for the proposed improvement through computer simulation using Matlab 11. | ||
| Keywords | ||
| voice; Excited LPC vocoder; Levinson; Durbin recursion; IIR filters; Discrete Cosine Transform; Mean square error | ||
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