Linear prediction: A tutorial review

Makhoul, John

doi:10.1109/proc.1975.9792

Cited by 3,338 publications

(705 citation statements)

References 70 publications

Supporting

Mentioning

679

Contrasting

Unclassified

Order By: Relevance

“…The time-varying vocal tract system parameters (i.e., Linear Prediction Coefficients (LPCs)) and the corresponding LP residual signal are derived from the speech signal by LP analysis [16]. The instants of significant excitation (GCIs) are computed from the LP residual using group delay analysis [17].…”

Section: Proposed Methods For Duration Modificationmentioning

confidence: 99%

Duration modification using glottal closure instants and vowel onset points

Rao

Yegnanarayana

2009

Speech Communication

View full text Add to dashboard Cite

This paper proposes a method for duration (time scale) modification using Glottal Closure Instants (GCI, also known as instants of significant excitation) and Vowel Onset Points (VOP). In general, most of the time scale modification methods attempt to vary the duration of speech segments uniformly over all regions. But it is observed that consonant regions and transition regions between a consonant and the following vowel, and between two consonant regions do not vary appreciably with speaking rate. The proposed method implements the duration modification without changing the durations of the transition and consonant regions. Vowel onset points are used to identify the transition and consonant regions. A VOP is the instant at which the onset of the vowel takes place, which corresponds to the transition from a consonant to the following vowel in most cases. The VOPs are computed using the Hilbert envelope of Linear Prediction (LP) residual. The instants of significant excitation correspond to the instants of glottal closure (epochs) in the case of voiced speech, and to some random excitations, like the onset of burst, in the case of nonvoiced speech. Manipulation of duration is achieved by modifying the duration of the LP residual with the help of instants of significant excitation as pitch markers. The modified residual is used to excite the time-varying filter whose parameters are derived from the original speech signal. Perceptual quality of the synthesized speech is found to be natural. Performance of the proposed method is compared with the method, where the duration of speech is modified uniformly over all regions. Samples of speech signals for different modification factors is available for listening at

show abstract

Section: Proposed Methods For Duration Modificationmentioning

confidence: 99%

Duration modification using glottal closure instants and vowel onset points

Rao

Yegnanarayana

2009

Speech Communication

View full text Add to dashboard Cite

show abstract

“…Different techniques have been employed by several researchers in a wide range of applications [36] such as neurophysics, electrocardiography, geophysics and speech communication.…”

Section: Linear Predictionmentioning

confidence: 99%

“…In Linear Prediction the objective is to predict or estimate the future output of a system based on the past output observations. The complete mathematical development and a compilation of the different Linear Prediction approaches have been presented by Makhoul [36].…”

Section: Linear Predictionmentioning

confidence: 99%

A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox

Elasha

Ruiz-Cárcel

David

et al. 2014

J Fail. Anal. and Preven.

View full text Add to dashboard Cite

“…AR models describe the original sequence as the output of filtering a temporally-uncorrelated (white) excitation sequence through a fixed length all-pole digital filter. Typically, AR models have been used in speech/audio applications for representing the envelope of the power spectrum of the signal by performing the operation of Time Domain Linear Prediction (TDLP) [5]. This paper utilizes AR models for obtaining smoothed, minimum phase, parametric models of temporal rather than spectral envelopes( Fig.…”

Section: Frequency Domain Linear Predictionmentioning

confidence: 99%

“…Since we apply the LP technique to exploit the redundancies in the frequency domain, we call this approach Frequency Domain Linear Prediction (FDLP) [4], [6]. For the FDLP technique, the squared magnitude response of the all-pole filter approximates the Hilbert envelope of the signal (in a manner similar to the approximation of the power spectrum of the signal by the [5]). …”

Section: Frequency Domain Linear Predictionmentioning

confidence: 99%

Hilbert Envelope Based Features for Far-Field Speech Recognition

Thomas

Ganapathy

Heřmanský

Machine Learning for Multimodal Interaction

View full text Add to dashboard Cite

Abstract. Automatic speech recognition (ASR) systems, trained on speech signals from close-talking microphones, generally fail in recognizing far-field speech. In this paper, we present a Hilbert Envelope based feature extraction technique to alleviate the artifacts introduced by room reverberations. The proposed technique is based on modeling temporal envelopes of the speech signal in narrow sub-bands using Frequency Domain Linear Prediction (FDLP). ASR experiments on far-field speech using the proposed FDLP features show significant performance improvements when compared to other robust feature extraction techniques (average relative improvement of 43% in word error rate).

show abstract

Linear prediction: A tutorial review

Cited by 3,338 publications

References 70 publications

Duration modification using glottal closure instants and vowel onset points

Duration modification using glottal closure instants and vowel onset points

A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox

Hilbert Envelope Based Features for Far-Field Speech Recognition

Contact Info

Product

Resources

About