The Theory of Linear Prediction

Vaidyanathan, P.P.

doi:10.2200/s00086ed1v01y200712spr03

Cited by 83 publications

(21 citation statements)

References 22 publications

(25 reference statements)

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“…LPC is a feature extraction technique within which a speech sample is approximated as a linear combination of past speech samples [17]. Linear prediction is mostly used in low bit rate transmission or storage of speech [16].…”

Section: Feature Extractionmentioning

confidence: 99%

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

Mustafa

Allen

Appiah

2017

Neural Comput & Applic

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The adoption of high-accuracy speech recognition algorithms without an effective evaluation of their impact on the target computational resource is impractical for mobile and embedded systems. In this paper, techniques are adopted to minimise the required computational resource for an effective mobile-based speech recognition system. A Dynamic MultiLayer Perceptron speech recognition technique, capable of running in real time on a state-of-the-art mobile device, has been introduced. Even though a conventional hidden Markov model when applied to the same dataset slightly outperformed our approach, its processing time is much higher. The Dynamic Multi-layer Perceptron presented here has an accuracy level of 96.94% and runs significantly faster than similar techniques.

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Section: Feature Extractionmentioning

confidence: 99%

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

Mustafa

Allen

Appiah

2017

Neural Comput & Applic

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“…The popular LPC algorithm determines the coefficients of a linear predictor by minimizing the prediction error in the least squares sense [21][22][23]. The MATLAB function lpc uses the autocorrelation method of autoregressive modeling to find prediction coefficients [22].…”

Section: Predictive Codingmentioning

confidence: 99%

Lossless predictive coding of electric signal waveforms

Duda¹

2017

Turk J Elec Eng & Comp Sci

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This paper describes a new predictive coding algorithm designed for lossless compression of electric signal waveforms. Prediction coefficients are obtained from the sinusoidal signal autoregressive model. Predictors for a single sinusoid and the sum of two and three sinusoids are considered. Integer-to-integer coding is obtained by the proper (i.e. reversible) quantization of computations. The proposed coding algorithm shows better performance than the commonly used 2nd order differences coding and linear prediction coding that is optimal in the least squares sense.

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“…Linear prediction theory dates back to the 1940s [33]. It has been used not only in speech processing, but also in neuroscience and geology [25].…”

Section: Linear Prediction Theorymentioning

confidence: 99%

Increasing anti-spoofing protection in speaker verification using linear prediction

Janicki

2016

Multimed Tools Appl

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This article addresses the problem of anti-spoofing protection in an automatic speaker verification (ASV) system. An improved version of a previously proposed spoofing countermeasure is presented. The presented method is based on the analysis of linear prediction error that results from both short-and long-term prediction of the input speech signal. It was observed that non-natural speech signals, i.e., synthetic or converted speech, were predicted in a different way than genuine speech. Therefore, in contrast to the classical linear prediction analysis, where usually only the prediction coefficients are analyzed, in the proposed approach the residual (error) signals were examined. During this analysis, 23 various prediction parameters were extracted, such as the energy of the prediction error, prediction gains and temporal parameters related to the prediction error signals. Various binary classifiers were researched to separate human and spoof classes, however the support vector machines with radial basis function (SVM-RBF) yielded the best results. When tested on the corpora provided for the ASVspoof 2015 Challenge, the proposed countermeasure returned better results than the previous version of the algorithm and, in most of the cases, the baseline spoofing detector based on the local binary patterns (LBP). It is hoped that the proposed method can be part of a generalized spoofing countermeasure helping to increase security of ASV systems.The calculations described in the article were made in the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) of the University of Warsaw (computational grant No. G46-2).

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The Theory of Linear Prediction

Cited by 83 publications

References 22 publications

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

Lossless predictive coding of electric signal waveforms

Increasing anti-spoofing protection in speaker verification using linear prediction

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