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Summary

This study enhances wearable device speech recognition using embedded audio positioning and enhancement algorithms. These methods improve accuracy by separating mixed audio sources, boosting performance in noisy environments.

Keywords:
embedded systemshybrid sound source separationposition detectionsignal-to-interference ratio (SIR)speech recognition

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Wearable devices and sensor networks are key in the IT industry for health and activity monitoring.
  • Enhancing speech recognition in embedded systems is crucial for improving user interaction with wearable technology.
  • Audio signal processing techniques are vital for overcoming environmental noise and interference in wearable devices.

Purpose of the Study:

  • To implement and evaluate audio positioning and enhancement algorithms for embedded systems.
  • To improve the speech recognition capabilities of wearable devices through advanced signal processing.
  • To demonstrate the effectiveness of direction detection and mixed source separation algorithms on different embedded platforms.

Main Methods:

  • Direction detection algorithm implemented on a TI TMS320C6713 DSK.
  • Mixed source separation algorithm developed and tested on a Raspberry Pi 2.
  • Experimental evaluation of mixed source separation using signal-to-interference ratio (SIR) and speech recognition accuracy.

Main Results:

  • The mixed source separation algorithm achieved an average SIR of 16.72 at 1m and 15.76 at 2m.
  • Speech recognition accuracy was significantly improved to 95% when using the developed audio enhancement algorithm.
  • Successful implementation of distinct audio processing algorithms on specialized embedded hardware.

Conclusions:

  • Embedded audio positioning and enhancement algorithms can significantly boost wearable device speech recognition.
  • The developed mixed source separation technique effectively improves audio quality in embedded systems.
  • This research provides a foundation for more robust and accurate voice-controlled wearable technologies.