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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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A Hybrid Speech Enhancement Algorithm for Voice Assistance Application.

Jenifa Gnanamanickam1, Yuvaraj Natarajan2, Sri Preethaa K R1

  • 1Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore 641407, India.

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Summary
This summary is machine-generated.

A novel hybridized speech recognition algorithm enhances accuracy by optimizing non-linear spectral subtraction with Hidden Markov Models. This method significantly reduces word error rates in noisy medical and emotional speech datasets.

Keywords:
speech enhancementspeech recognitionspeech to textword error rate

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Area of Science:

  • Speech Processing
  • Artificial Intelligence
  • Signal Processing

Background:

  • Speech recognition systems perform poorly in real-world noisy environments.
  • Background and channel noise degrade speech intelligibility and accuracy.
  • Existing speech enhancement algorithms are limited to simple, continuous audio signals.

Purpose of the Study:

  • To propose a hybridized speech recognition algorithm for improved accuracy.
  • To address the limitations of current enhancement methods in complex audio.
  • To reduce external noise for more precise speech-to-text conversion.

Main Methods:

  • A hybrid algorithm combining non-linear spectral subtraction and Hidden Markov Models was developed.
  • The model was trained and tested on 6660 medical speech and 1440 RAVDESS audio files.
  • Performance was compared against iterative signal enhancement, subspace-based enhancement, and non-linear spectral subtraction.

Main Results:

  • The proposed cascaded hybrid algorithm achieved a minimum word error rate of 9.5% for medical speech.
  • It achieved a minimum word error rate of 7.6% for RAVDESS speech.
  • The enhanced speech recognition demonstrated higher accuracy through cascaded architectures.

Conclusions:

  • The hybridized approach significantly improves speech recognition accuracy in noisy conditions.
  • The method is suitable for real-time applications, especially in complex medical dictation.
  • Cascading speech enhancement with speech-to-text conversion architectures is effective.