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Linear predictive coding

About: Linear predictive coding is a research topic. Over the lifetime, 6565 publications have been published within this topic receiving 142991 citations. The topic is also known as: Linear predictive coding, LPC.


Papers
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Proceedings ArticleDOI
15 Mar 1999
TL;DR: The perceptual phase capacity in low pitched speech is found to be much higher than it is for high pitched speech, which is consistent with the well known fact that speech coding schemes which preserve the phase accurately work better for male voices, while coders which put more weight on the amplitude spectrum of the speech signal result in better quality for female speech.
Abstract: In this paper we define perceptual phase capacity as the size of a codebook of phase spectra necessary to represent all possible phase spectra in a perceptually accurate manner. We determine the perceptual phase capacity for voiced speech. To this purpose, we use an auditory model which indicates if phase spectrum changes are audible or not. The correct performance of the model was adjusted and verified by listening tests. The perceptual phase capacity in low pitched speech is found to be much higher than it is for high pitched speech. Our results are consistent with the well known fact that speech coding schemes which preserve the phase accurately work better for male voices, while coders which put more weight on the amplitude spectrum of the speech signal result in better quality for female speech.

57 citations

Proceedings ArticleDOI
21 Apr 1997
TL;DR: A variable time-scale modification method based on the knowledge that the timing information of transient portions of a speech signal plays an important role in speech perception gets the target rate by modifying steady portions only.
Abstract: Conventional time-scale modification methods have the problem that as the modification rate gets higher the time-scale modified speech signal becomes less intelligible, because they ignore the effect of articulation rate on speech characteristics. We propose a variable time-scale modification method based on the knowledge that the timing information of transient portions of a speech signal plays an important role in speech perception. After identifying transient and steady portions of a speech signal, the proposed method gets the target rate by modifying steady portions only. The result of subjective preference test indicates that the proposed method produces performance superior to that of the conventional SOLA method.

57 citations

PatentDOI
TL;DR: In this article, the residual signal derived from linear predictive coding (LPC) estimation is adaptively filtered, and then is used as the input to a conventional pitch estimation procedure, where the adaptive filtering step uses the first reflection coefficient (k1) to realize a simple filter (e.g., A(z)=(1-k1 z-1)-1).
Abstract: A voice messaging system, wherein linear predictive coding (LPC) parameters, pitch, and preferably other excitation information is derived from a human voice input, encoded, and transmitted and/or stored, to be called up later to provide a speech output which is nearly identical to the original speech input. The invention features adaptive filtering of the residual signal. The residual signal derived from LPC estimation is adaptively filtered, and then is used as the input to a conventional pitch estimation procedure. The adaptive filtering step uses the first reflection coefficient (k1) to realize a simple filter (e.g., A(z)=(1-k1 z-1)-1. This filter removes high frequency noise from the residual signal during voiced periods, but does not remove the high frequency energy which contains important information during the unvoiced periods of speech. Preferably the above preprocessing technique is also combined with a postprocessing technique, wherein dynamic programming is used to optimally track pitch and voicing information through successive frames.

57 citations

Proceedings Article
01 Jan 1998
TL;DR: It is observed that by selectively combining the cepstral streams representing the LPC parameters and the residual signal it is possible to obtain recognition accuracy directly from the coded parameters that equals or exceeds the recognition accuracy obtained from the reconstructed waveforms.
Abstract: Speech coding affects speech recognition performance, with recognition accuracy deteriorating as the coded bit rate decreases. Virtually all systems that recognize coded speech reconstruct the speech waveform from the coded parameters, and then perform recognition (after possible noise and/or channel compensation) using conventional techniques. In this paper we compare the recognition accuracy of coded speech obtained by reconstructing the speech waveform with the speech recognition accuracy obtained when using cepstral features derived from the coding parameters. We focus our efforts on speech that has been coded using the 13-kbps full-rate GSM codec, a Regular Pulse Excited Long Term Prediction (RPE-LTP) codec. The GSM codec develops separate representations for the linear prediction (LPC) filter and the residual signal components of the coded speech. We measure the effects of quantization and coding on the accuracy with which these parameters are represented, and present two different methods for recombining them for speech recognition purposes. We observe that by selectively combining the cepstral streams representing the LPC parameters and the residual signal it is possible to obtain recognition accuracy directly from the coded parameters that equals or exceeds the recognition accuracy obtained from the reconstructed waveforms.

56 citations

01 Jan 2003
TL;DR: In this article, the authors present a brief overview of the speech enhancement problem for wide-band noise sources that are not correlated with the speech signal, focusing on the spectral subtraction approach and some of its derivatives in the forms of linear and non-linear minimum mean square error estimators.
Abstract: We present a brief overview of the speech enhancement problem for wide-band noise sources that are not correlated with the speech signal. Our main focus is on the spectral subtraction approach and some of its derivatives in the forms of linear and non-linear minimum mean square error estimators. For the linear case, we review the signal subspace approach, and for the non-linear case, we review spectral magnitude and phase estimators. On line estimation of the second order statistics of speech signals using parametric and non-parametric models is also addressed.

56 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20239
202225
202126
202042
201925
201837