Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners.

Journal: The Journal of the Acoustical Society of America
PMID: 28372043

Abstract

Machine-learning based approaches to speech enhancement have recently shown great promise for improving speech intelligibility for hearing-impaired listeners. Here, the performance of three machine-learning algorithms and one classical algorithm, Wiener filtering, was compared. Two algorithms based on neural networks were examined, one using a previously reported feature set and one using a feature set derived from an auditory model. The third machine-learning approach was a dictionary-based sparse-coding algorithm. Speech intelligibility and quality scores were obtained for participants with mild-to-moderate hearing impairments listening to sentences in speech-shaped noise and multi-talker babble following processing with the algorithms. Intelligibility and quality scores were significantly improved by each of the three machine-learning approaches, but not by the classical approach. The largest improvements for both speech intelligibility and quality were found by implementing a neural network using the feature set based on auditory modeling. Furthermore, neural network based techniques appeared more promising than dictionary-based, sparse coding in terms of performance and ease of implementation.

Authors

  • Jessica J M Monaghan
    ISVR, University of Southampton, University Rd, Southampton SO17 1BJ, United Kingdom.
  • Tobias Goehring
    ISVR, University of Southampton, University Rd, Southampton SO17 1BJ, United Kingdom. Electronic address: goehring.tobias@gmail.com.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Federico Bolner
    ExpORL, KU Leuven, O&N II Herestraat 49, 3000 Leuven, Belgium; Cochlear Technology Centre, Schaliënhoevedreef 20 I, 2800 Mechelen, Belgium.
  • Shangqiguo Wang
    Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
  • Matthew C M Wright
    Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
  • Stefan Bleeck
    ISVR, University of Southampton, University Rd, Southampton SO17 1BJ, United Kingdom.

Keywords

External Resources

Popular Topics
Recent Journals