Estimating the speaking rate by vowel detection

Pfau, Thilo; Ruske, Günther

doi:10.1109/icassp.1998.675422

Cited by 53 publications

(40 citation statements)

References 3 publications

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“…, C} are the numbers of vowels from a given alphabet. To solve the ASR task, query utterance X is automatically divided into K syllables (Janakiraman et al, 2010) and the vowel segment X(k) is extracted from the k-th syllable (Pfau and Ruske, 1998). We assume that syllables are extracted without mistakes, e.g., the voice commands are produced in isolated syllable mode (Merialdo, 1988).…”

Section: Voice Command Recognitionmentioning

confidence: 99%

Statistical Testing of Segment Homogeneity in Classification of Piecewise–Regular Objects

Savchenko

Belova

2015

International Journal of Applied Mathematics and Computer Science

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The paper is focused on the problem of multi-class classification of composite (piecewise-regular) objects (e.g., speech signals, complex images, etc.). We propose a mathematical model of composite object representation as a sequence of independent segments. Each segment is represented as a random sample of independent identically distributed feature vectors. Based on this model and a statistical approach, we reduce the task to a problem of composite hypothesis testing of segment homogeneity. Several nearest-neighbor criteria are implemented, and for some of them the well-known special cases (e.g., the Kullback-Leibler minimum information discrimination principle, the probabilistic neural network) are highlighted. It is experimentally shown that the proposed approach improves the accuracy when compared with contemporary classifiers.

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Section: Voice Command Recognitionmentioning

confidence: 99%

Statistical Testing of Segment Homogeneity in Classification of Piecewise–Regular Objects

Savchenko

Belova

2015

International Journal of Applied Mathematics and Computer Science

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“…False alarms were reduced by rejecting peaks lower than a threshold and by rejecting all but one peak from sets of peaks that are too close in time. In Pfau and Ruske [1998] syllable nuclei were detected using a loudness function similar to Zwicker et al [1979], and false peaks were filtered in two ways. First, peaks that were not sufficiently close to a neighboring decline in the energy curve were removed.…”

Section: Syllable Detectionmentioning

confidence: 99%

“…The work in Dashiell et al [2008] combined nuclei estimations from a HMM based manner classifier, an energy curve method from Pfau and Ruske [1998], and the correlation based method from Wang and Narayanan Narayanan [2005, 2007]. It also estimated syllable boundaries by combining the manner classifier, the energy curve method from Pfau and Ruske [1998], and using minima of the spectral discontinuity features from Wu et al [1997], Shire [1997].…”

Section: Syllable Detectionmentioning

confidence: 99%

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Graphical models for integrating syllabic information

Bartels

Bilmes

2010

Computer Speech & Language

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“…A recognizer that can distinguish between vowels and consonants, e.g., a broad phonetic class recognizer, or a vowel detection algorithm [10,11], would be sufficient for estimating speaking rate. The broad phonetic classes, e.g., nasals, stops, vowels, etc., possess more distinct spectral characteristics than the phones within the same broad phonetic classes.…”

Section: Introductionmentioning

confidence: 99%

Robust speaking rate estimation using broad phonetic class recognition

Yuan

Liberman

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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Robust speaking rate estimation can be useful in automatic speech recognition and speaker identification, and accurate, automatic measures of speaking rate are also relevant for research in linguistics, psychology, and social sciences. In this study we built a broad phonetic class recognizer for speaking rate estimation. We tested the recognizer on a variety of data sets, including laboratory speech, telephone conversations, foreign accented speech, and speech in different languages, and we found that the recognizer's estimates are robust under these sources of variation. We also found that the acoustic models of the broad phonetic classes are more robust than those of the monophones for syllable detection. KeywordsSpeaking rate estimation, syllable detection, robustness, broad phonetic class ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/444 ROBUST SPEAKING RATE ESTIMATION USING BROAD PHONETIC CLASS RECOGNITION Jiahong Yuan and Mark LibermanUniversity of Pennsylvania ABSTRACTRobust speaking rate estimation can be useful in automatic speech recognition and speaker identification, and accurate, automatic measures of speaking rate are also relevant for research in linguistics, psychology, and social sciences. In this study we built a broad phonetic class recognizer for speaking rate estimation. We tested the recognizer on a variety of data sets, including laboratory speech, telephone conversations, foreign accented speech, and speech in different languages, and we found that the recognizer's estimates are robust under these sources of variation. We also found that the acoustic models of the broad phonetic classes are more robust than those of the monophones for syllable detection.

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Estimating the speaking rate by vowel detection

Cited by 53 publications

References 3 publications

Statistical Testing of Segment Homogeneity in Classification of Piecewise–Regular Objects

Statistical Testing of Segment Homogeneity in Classification of Piecewise–Regular Objects

Graphical models for integrating syllabic information

Robust speaking rate estimation using broad phonetic class recognition

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