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A probabilistic framework for landmark detection based on phonetic features for automatic speech recognition.

Amit Juneja1, Carol Espy-Wilson

  • 1Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742, USA. amjuneja@gmail.com

The Journal of the Acoustical Society of America
|February 6, 2008
PubMed
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This study introduces a probabilistic framework for speech recognition using acoustic parameters to detect phonetic landmarks. The approach enhances continuous speech recognition accuracy by modeling phonetic features.

Area of Science:

  • Speech Recognition
  • Phonetics
  • Acoustic Phonetics

Background:

  • Traditional speech recognition systems often rely on Mel-frequency cepstral coefficients (MFCCs).
  • Acoustic parameters (APs) offer a different approach by directly capturing phonetic features.
  • Landmark-based methods focus on identifying key points in speech signals.

Purpose of the Study:

  • To present a novel probabilistic framework for landmark-based speech recognition.
  • To utilize acoustic parameters (APs) for detecting phonetic landmarks related to manner features.
  • To evaluate the performance of this framework against established methods.

Main Methods:

  • Developed a probabilistic framework for detecting multiple landmark sequences in continuous speech.

Related Experiment Videos

  • Employed acoustic parameters (APs) capturing manner-based phonetic features (syllabic, sonorant, continuant).
  • Used binary classifiers for probabilistic landmark detection and constrained sequences with pronunciation models.

    • Main Results:

    • The probabilistic landmark detection system utilizing APs demonstrated competitive performance.
    • Comparison with MFCC-based probabilistic and Hidden Markov Model (HMM) systems was conducted.
    • The framework's ability to exploit feature sufficiency and invariance was highlighted.

    Conclusions:

    • The proposed probabilistic framework offers a viable alternative for speech recognition.
    • Acoustic parameters provide valuable information for phonetic landmark detection.
    • Further research can explore integrating this framework with other speech recognition paradigms.