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Attentive Training: A New Training Framework for Speech Enhancement.

Ashutosh Pandey1, DeLiang Wang2

  • 1Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210 USA.

IEEE/ACM Transactions on Audio, Speech, and Language Processing
|October 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Attentive Training, a new method to improve speech enhancement systems by focusing on the first speaker in noisy environments. This approach effectively handles speech interruptions in single-output systems.

Keywords:
attentive trainingspeaker extractionspeaker separationspeech enhancementtalker-independent

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Area of Science:

  • Speech processing
  • Deep learning
  • Signal processing

Background:

  • Speech enhancement systems often struggle with multiple speakers and noise.
  • Existing methods like speaker separation or extraction have limitations for single-output systems.

Purpose of the Study:

  • To propose a novel training framework, Attentive Training, for speech enhancement systems.
  • To enable speech enhancement systems to effectively handle speech interruptions from multiple speakers.

Main Methods:

  • Developed a novel training framework named Attentive Training.
  • Trained a deep neural network to represent and track the first speaker in a noisy mixture.

Main Results:

  • Demonstrated the effectiveness of Attentive Training for speech enhancement.
  • Showcased the ability to attend to and track a target speaker in multitalker noisy conditions.

Conclusions:

  • Attentive Training is a viable solution for enhancing speech in the presence of interruptions.
  • The proposed method is suitable for standalone speech enhancement systems with a single output stream.