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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Published on: September 27, 2024

Modulation frequency features for phoneme recognition in noisy speech.

Sriram Ganapathy1, Samuel Thomas, Hynek Hermansky

  • 1Idiap Research Institute, Martigny, Switzerland. ganapathy@idiap.ch

The Journal of the Acoustical Society of America
|January 29, 2009
PubMed
Summary
This summary is machine-generated.

A novel speech analysis method using modulation spectrum features improves phoneme recognition in telephone speech. This technique enhances performance without compromising accuracy in clean environments.

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Area of Science:

  • Speech processing
  • Machine learning
  • Signal analysis

Background:

  • Traditional speech recognition methods face challenges with telephone speech quality.
  • Feature extraction is crucial for accurate phoneme identification.

Purpose of the Study:

  • To introduce a new feature extraction technique for improved phoneme recognition in telephone speech.
  • To evaluate the proposed method against existing state-of-the-art techniques.

Main Methods:

  • Feature extraction based on modulation spectrum of subband temporal envelopes.
  • Autoregressive modeling of Hilbert envelopes in critical bands.
  • Application of static and dynamic compression to subband envelopes.
  • Machine recognition of phonemes using the extracted features.

Main Results:

  • Significant improvements in phoneme recognition rates for telephone speech.
  • Comparable performance to existing methods in clean speech conditions.
  • Detailed performance analysis across broad phonetic classes.

Conclusions:

  • The proposed modulation spectrum-based features offer a robust solution for telephone speech recognition.
  • This technique advances the field of speech analysis and machine recognition.
  • The method demonstrates superior performance, particularly in noisy or degraded speech conditions.