On the relative importance of various components of the modulation spectrum for automatic speech recognition

Noboru Kanedera, Takayuki Arai, Hynek Hermansky, Misha Pavel

Research output: Contribution to journalArticle

100 Scopus citations

Abstract

We measured the accuracy of speech recognition as a function of band-pass filtering of the time trajectories of spectral envelopes. We examined (i) several types of recognizers such as dynamic time warping (DTW) and hidden Markov model (HMM), and (ii) several types of features, such as filter bank output, mel-frequency cepstral coefficients (MFCC), and perceptual linear predictive (PLP) coefficients. We used the resulting recognition data to determine the relative importance of information in different modulation spectral components of speech for automatic speech recognition. We concluded that: (1) most of the useful linguistic information is in modulation frequency components from the range between 1 and 16 Hz, with the dominant component at around 4 Hz; (2) in some realistic environments, the use of components from the range below 2 Hz or above 16 Hz can degrade the recognition accuracy.

Original languageEnglish (US)
Pages (from-to)43-55
Number of pages13
JournalSpeech Communication
Volume28
Issue number1
DOIs
StatePublished - May 1999

    Fingerprint

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this