Real-Time Recognition of Spoken Words

Pols, L.C.W.

doi:10.1109/t-c.1971.223391

Cited by 47 publications

(11 citation statements)

References 25 publications

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“…Pols (1971Pols ( ,1975Pols ( ,1977; and Nierop et al (1973)]. In all cases, the statistical properties of speech were summarized using a variance-covariance matrix corresponding to level-normalized, log-coded speech spectra, although there is no firm experimental justification for this particular coding choice.…”

Section: Introductionmentioning

confidence: 99%

Principal-components analysis for low-redundancy encoding of speech spectra

Zahorian

Rothenberg

1981

The Journal of the Acoustical Society of America

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The principal-components statistical procedure for data reduction is used to efficiently encode speech power spectra by exploiting the correlations of power spectral amplitudes at various frequencies. Although this datareduction procedure has been used in several previous studies, little attempt was made to optimize the methods for spectral selection and coding through the use of intelligibility testing. In the present study, principal-components basis vectors were computed from the continuous speech of several male and female speakers using various nonlinear spectral amplitude scales. Speech was synthesized using a combination linear predictive (LP) principal-components vocoder. Of the amplitude scales investigated for use with a principalcomponents analysis of speech spectra, logarithmic amplitude coding of non-normalized spectra emerged as a slight favorite. Speech synthesized from four principal components was found to be about 80% intellig/•ble using a form of the Diagnostic Rhyme Test for rhyming word pairs and about 95% intelligible, for words within a sentence context. Speech synthesized from spectral principal components compared favorably in intelligibility and quality with speech synthesized from a control LP vocoder with the same number of parameters.

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Section: Introductionmentioning

confidence: 99%

Principal-components analysis for low-redundancy encoding of speech spectra

Zahorian

Rothenberg

1981

The Journal of the Acoustical Society of America

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“…In addition, they are close to the principal component decomposition of the speech spectrum (Pols, van der Kamp, & Plomp, 1969) and have been found to be well correlated with perceptual judgment data (Pols, 1971).…”

Section: Melcepstral Coefficientsmentioning

confidence: 56%

Automatic Recognition of Speech in Stressful Environments

Clements¹,

Hansen²,

Cummings³

et al. 1991

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“…The purpose of reducing the feature dimensionality is to reduce the required storage for reference and test patterns. This method has also been used by Pols 7 to recognize vowels with good success.…”

Section: Principal Components Analysismentioning

confidence: 99%