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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Joint Dictionary Learning-Based Non-Negative Matrix Factorization for Voice Conversion to Improve Speech

Szu-Wei Fu1, Pei-Chun Li2, Ying-Hui Lai3

  • 1Department of Computer Science and Information EngineeringNational Taiwan University.

IEEE Transactions on Bio-Medical Engineering
|December 28, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new machine learning method, joint dictionary learning based non-negative matrix factorization (JD-NMF), to improve speech clarity for surgical patients. The JD-NMF technique enhances intelligibility even with limited training data, offering a significant advancement for communication.

Keywords:
Data modelsDictionariesSpectrogramSpeechSurgeryTrainingTraining data

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Area of Science:

  • Machine Learning
  • Speech Processing
  • Bioacoustics

Background:

  • Surgical removal of articulators can lead to distorted speech, impacting patient communication.
  • Existing voice conversion (VC) methods face challenges with limited training data and require rapid conversion for practical use.

Purpose of the Study:

  • To develop an effective machine learning-based voice conversion (VC) technique for improving speech intelligibility in surgical patients.
  • To address the limitations of small training datasets and the need for efficient conversion in post-operative communication.

Main Methods:

  • A novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm is proposed.
  • The JD-NMF method is designed for efficient and effective VC with limited training data.

Main Results:

  • The JD-NMF method significantly improves short-time objective intelligibility (STOI) scores compared to original speech.
  • Experimental results show JD-NMF is more efficient and effective than conventional exemplar-based NMF VC methods.

Conclusions:

  • The proposed JD-NMF method demonstrates superior performance in enhancing speech intelligibility for oral surgery patients.
  • The joint training criterion for NMF-based VC is validated, confirming the effectiveness of JD-NMF.