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Crosstalk Suppression in a Multi-Channel, Multi-Speaker System Using Acoustic Vector Sensors.

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This study introduces a new algorithm for multi-speaker speech recognition in noisy environments. It effectively reduces crosstalk using Acoustic Vector Sensors, improving speech-to-text accuracy.

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Area of Science:

  • Signal Processing
  • Acoustics
  • Machine Learning

Background:

  • Automatic speech recognition (ASR) in multi-speaker, reverberant environments like courtrooms presents significant challenges.
  • Crosstalk, or interference between speakers, degrades ASR performance and requires pre-processing for accurate speech-to-text transcription.

Purpose of the Study:

  • To develop and evaluate an algorithm for crosstalk suppression in multi-speaker ASR scenarios.
  • To enhance the performance of speech-to-text systems in challenging acoustic conditions such as conferences and court sessions.

Main Methods:

  • Utilized Acoustic Vector Sensors for acquiring audio streams in multi-speaker environments.
  • Employed statistical analysis of Direction of Arrival (DOA) for speaker detection and source separation.
  • Implemented a dynamic gain processor for crosstalk suppression after analyzing speaker activity and identifying signal fragments.

Main Results:

  • Achieved significant improvements in Scale-Invariant Signal-to-Distortion Ratio (SI-SDR).
  • Demonstrated an increase of 7.54 dB in SI-SDR with the full algorithm (including source separation).
  • Showcased an even higher increase of 19.53 dB in SI-SDR when only crosstalk suppression was applied.

Conclusions:

  • The proposed algorithm effectively suppresses crosstalk in multi-speaker reverberant environments.
  • The method enhances audio quality for subsequent speech-to-text transcription, showing promise for real-world applications.
  • Source separation and dynamic gain processing are key components for achieving substantial performance gains.