Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 1/2013

01.03.2013

Phoneme recognition using zerocrossing interval distribution of speech patterns and ANN

verfasst von: R. K. Sunil Kumar, V. L. Lajish

Erschienen in: International Journal of Speech Technology | Ausgabe 1/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The speech signal is modeled using zerocrossing interval distribution of the signal in time domain. The distributions of these parameters are studied over five Malayalam (one of the most popular Indian language) vowels. We found that the distribution patterns are almost similar for repeated utterances of the same vowel and varies from vowel to vowel. These distribution patterns are used for recognizing the vowels using multilayer feed forward artificial neural network. After analyzing the distribution patterns and the vowel recognition results, we realize that the zerocrossing interval distribution parameters can be effectively used for the speech phone classification and recognition. The noise adaptness of this parameter is also studied by adding additive white Gaussian noise at different signal to noise ratio. The computational complexity of the proposed technique is also less compared to the conventional spectral techniques, which includes FFT and Cepstral methods, used in the parameterization of speech signal.
Vorheriger Artikel Improving the performance of speaker and language identification tasks using unique characteristics of a class

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Arai, T., & Yoshida, Y. (1990). Study on zerocrossing of speech signals by means of analytic signal. Journal of Acoustical Society of Japan, 692, 242–246.
Bui, N. C., Jmonbaron, J., & Michel, J. G. (1983). An integrated voice recognition system. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-31, 323–328.
Chandrasekhar, C. (1996). Neural network models for recognition of stop consonant vowel(scv) segments in continuous speech. PhD thesis, Department of Computer Science and Engg, IIT, Madras, India.
Chandrasekhar, C., & Yegnanarayana, B. (1996). Recognition of stop–consonant–vowel (SCV) segments in continuous speech using neural network models. Journal of Institution of Electronics and Telecommunication Engineers, 42, 269–280.
Cristi, R. (2007). Modern digital signal processing. Washington: Thomson Brooks/Cole.
Erdol, N., Castelluccia, C., & Zilouchian, A. (1993). Recovery of missing speech packet using the short time energy and zerocrossing measurements. IEEE Transactions on Audio and Electroacoustics, 1.1(3), 295–303.
Hecht-Nielsen, R. (1990). Neurocomputing. Reading: Addison-Wesley.
Itakura, F. (1975). Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-23, 67–72. CrossRef
Kim, K.-S., & Hwang, H.-Y. (1991). A study on the speech recognition of Korean phonemes using recurrent neural network models. Transactions of the Korean Institute of Electrical Engineers, 40(8), 782–791.
Kim, D.-S., Jeong, J. H., Kim, J. W., & Lee, S.Y. (1996). Feature extraction based on zerocrossing with peak amplitudes for robust speech recognition in noisy environments. IEEE Transactions on Audio and Electroacoustics, AU 17, 61–64.
Kwok, H. L., Tai, T. C., & Fung, Y. M. (1983). Machine recognition of the Cantonese digits using band pass filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-31, 220–222. CrossRef
Lau, Y.-K., & Chan, C.-K. (1985). Speech recognition based on zerocrossing rate and energy. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(1).
Licklider, J. C. R. (1950). Intelligibility of amplitude-dichotomized time quantized speech waves. The Journal of the Acoustical Society of America, 22(5), 820–823. CrossRef
Licklider, J. C. R., & Pollack, I. (1948). Effects of differentiation, integration and infinite pack clipping upon intelligibility of speech. The Journal of the Acoustical Society of America, 20, 42. CrossRef
Niederjohn, R. J. (1975). A mathematical formulation and comparison of zerocrossing analysis techniques which have been applied to automatic speech recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-23(4), 373–380. CrossRef
Niederjohn, R. J., & Lahat, M. (1985). A zero-crossing consistency method for formant tracking of voiced speech in high noise levels. IEEE Transactions on Acoustics, Speech, and Signal Processing, 2, 349–355. CrossRef
Niederjohn, R. J., Krutz, M. W., & Brown, B. M. (1987). An experimental investigation of perceptual effects of altering the zerocrossing of a speech signal. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-35(5), 618–625. CrossRef
Rabiner, L. R., & Juang, B.H. (1993). Fundamentals of speech recognition. New York: Prentice Hall.
Sreenivas, T. V., & Niederjohn, R. J. (1992). Zerocrossing based spectral analysis and SVD spectral analysis for formant frequency estimation in noise. IEEE Transactions on Signal Processing, 40(2).
Wasson, D. A., & Donaldson, R. W. (1975). Speech amplitude and zerocrossing for automatic identification of human speakers. IEEE Transactions on Acoustics, Speech, and Signal Processing, 390–392.
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. (1988). Phoneme recognition: neural networks vs. hidden Markov models. IEEE Transactions on Neural Networks, 18(2), 107–110.
Metadaten
Titel
Phoneme recognition using zerocrossing interval distribution of speech patterns and ANN
verfasst von
R. K. Sunil Kumar
V. L. Lajish
Publikationsdatum
01.03.2013
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 1/2013
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-012-9169-x

Weitere Artikel der Ausgabe 1/2013

International Journal of Speech Technology 1/2013 Zur Ausgabe

OriginalPaper

Blind separation of audio signals using trigonometric transforms and Kalman filtering

OriginalPaper

Development and evaluation of online text-independent speaker verification system for remote person authentication

OriginalPaper

Dynamic prosody modification using zero frequency filtered signal

OriginalPaper

Speaker recognition utilizing distributed DCT-II based Mel frequency cepstral coefficients and fuzzy vector quantization

OriginalPaper

Effect of aging on speech features and phoneme recognition: a study on Bengali voicing vowels

OriginalPaper

Emotion modeling from speech signal based on wavelet packet transform

Neuer Inhalt

    Bildnachweise