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.

Line spectral frequencies and energy features in a robust signal recognition system

Abstract: One embodiment of a speech recognition system is organized with speech input signal preprocessing and feature extraction followed by a fuzzy matrix quantizer (FMQ). Frames of the speech input signal are represented in a matrix by a vectorf of line spectral pair frequencies and energy coefficients and are fuzzy matrix quantized to respective vector f entries of a matrix codeword in a codebook of the FMQ. The energy coefficients include the original energy and the first and second derivatives of the original energy which increase recognition accuracy by, for example, being generally distinctive speech input signal parameters and providing noise signal suppression especially when the noise signal has a relatively constant energy over at least two time frame intervals. To reduce data while maintaining sufficient resolution, the energy coefficients may be normalized and logarithmically represented. A distance measure between f and f, d(f, f), is defined as ##EQU1## where the constants α1, α2, β1 and β2 are set to substantially minimize quantization error, ei is the error power spectrum of the speech input signal and a predicted speech input signal at the ith line spectral pair frequency of the speech input signal, the first G LSP frequencies are most likely to be frequency shifted by noise, and the last P+3 coefficients represent the three energy coefficients. This robust distance measure can be used to enhance speech recognition performance in generally any speech recognition system using line spectral pair based distance measures.

Fast CELP coding based on the Barnes-Wall lattice in 16 dimensions

Abstract: The authors present an algebraic code-excited linear prediction (CELP) speech coder where the innovation codebook comes from the first spherical code of the Barnes-Wall lattice in 16 dimensions. Novel fast optimal algorithms for finding the best sequence in this Barnes-Wall shell innovation codebook are described. This algebraic codebook makes it possible to design a CELP coder at 9.6 kb/s with good quality and still implementable on a current digital-signal-processing chip. >

Optimized estimation of spectral parameters for the coding of noisy speech

Abstract: In this contribution we optimize a speech enhancement preprocessor such that a distortion measure in the line spectral frequency (LSF) domain is minimized. We can thus improve the estimation of spectral parameters of a speech coder when the input signal to the coder is a noisy speech signal. The optimization aims at the maximum noise reduction of the enhancement preprocessor. The average maximum noise reduction characteristic is determined as a function of the speech signal SNR and is approximated by an exponential function. Since LSF parameters are widely used in speech coding the results are applicable to a wide range of speech coders and enhancement preprocessors. We report experimental results for an MMSE log spectral amplitude estimator in conjunction with the new ETSI adaptive multi-rate (AMR) speech coder. We found that the method is most effective for the low bit rate coding modes.

On the use of different feature extraction methods for linear and non linear kernels

Abstract: The speech feature extraction has been a key focus in robust speech recognition research; it significantly affects the recognition performance. In this paper, we first study a set of different feature extraction methods such as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) with several features normalization techniques including rasta filtering and cepstral mean subtraction (CMS). Based on this, a comparative evaluation of these features is performed on the task of text independent speaker identification using a combination between gaussian mixture models (GMM) and linear or non-linear kernels based on support vector machine (SVM).