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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
More filters
Journal ArticleDOI
TL;DR: A speech recognition model is proposed in which the transformation from an input speech signal into a sequence of phonemes is carried out largely through an active or feedback process.
Abstract: A speech recognition model is proposed in which the transformation from an input speech signal into a sequence of phonemes is carried out largely through an active or feedback process. In this process, patterns are generated internally in the analyzer according to an adaptable sequence of instructions until a best match with the input signal is obtained. Details of the process are given, and the areas where further research is needed are indicated.

278 citations

Journal ArticleDOI
TL;DR: A class of Kalman filter-based algorithms with some extensions, modifications, and improvements of previous work is presented, including the estimate-maximize (EM) method to iteratively estimate the spectral parameters of the speech and noise parameters.
Abstract: Speech quality and intelligibility might significantly deteriorate in the presence of background noise, especially when the speech signal is subject to subsequent processing. In particular, speech coders and automatic speech recognition (ASR) systems that were designed or trained to act on clean speech signals might be rendered useless in the presence of background noise. Speech enhancement algorithms have therefore attracted a great deal of interest. In this paper, we present a class of Kalman filter-based algorithms with some extensions, modifications, and improvements of previous work. The first algorithm employs the estimate-maximize (EM) method to iteratively estimate the spectral parameters of the speech and noise parameters. The enhanced speech signal is obtained as a byproduct of the parameter estimation algorithm. The second algorithm is a sequential, computationally efficient, gradient descent algorithm. We discuss various topics concerning the practical implementation of these algorithms. Extensive experimental study using real speech and noise signals is provided to compare these algorithms with alternative speech enhancement algorithms, and to compare the performance of the iterative and sequential algorithms.

276 citations

Proceedings ArticleDOI
12 Apr 1976
TL;DR: A rationale is advanced for digitally coding speech signals in terms of sub-bands of the total spectrum, which provides a means for controlling and reducing quantizing noise in the coding.
Abstract: A rationale is advanced for digitally coding speech signals in terms of sub-bands of the total spectrum. The approach provides a means for controlling and reducing quantizing noise in the coding. Each sub-band is quantized with an accuracy (bit allocation) based upon perceptual criteria. As a result, the quality of the coded signal is improved over that obtained from a single full-band coding of the total spectrum. In one implementation, the individual sub-bands are low-pass translated before coding. In another, "integer-band" sampling is employed to alias the signal in an advantageous way before coding. Other possibilities extend to complex demodulation of the sub-bands, and to representing the subband signals in terms of envelopes and phase-derivatives. In all techniques, adaptive quantization is used for the coding, and a parsimonious allocation of bits is made across the bands. Computer simulations are made to demonstrate the signal qualities obtained for codings at 16 and 9.6 Kbits/sec.

276 citations

Journal ArticleDOI
TL;DR: It is suggested that recent studies based on a Source Generator Framework can provide a viable foundation in which to establish robust speech recognition techniques, and three novel approaches for signal enhancement and stress equalization are considered to address the issue of recognition under noisy stressful conditions.

270 citations

Journal ArticleDOI
TL;DR: The algorithms are evaluated with respect to improving automatic recognition of speech in the presence of additive noise and shown to outperform other enhancement methods in this application.
Abstract: The basis of an improved form of iterative speech enhancement for single-channel inputs is sequential maximum a posteriori estimation of the speech waveform and its all-pole parameters, followed by imposition of constraints upon the sequence of speech spectra. The approaches impose intraframe and interframe constraints on the input speech signal. Properties of the line spectral pair representation of speech allow for an efficient and direct procedure for application of many of the constraint requirements. Substantial improvement over the unconstrained method is observed in a variety of domains. Informed listener quality evaluation tests and objective speech quality measures demonstrate the technique's effectiveness for additive white Gaussian noise. A consistent terminating point of the iterative technique is shown. The current systems result in substantially improved speech quality and linear predictive coding (LPC) parameter estimation with only a minor increase in computational requirements. The algorithms are evaluated with respect to improving automatic recognition of speech in the presence of additive noise and shown to outperform other enhancement methods in this application. >

263 citations


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