Journal
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
Volume 141, Issue 3, Pages 1985-1998Publisher
ACOUSTICAL SOC AMER AMER INST PHYSICS
DOI: 10.1121/1.4977197
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Funding
- EPSRC [EP/K020501/1]
- European Union's Seventh Framework Programme (ITN ICanHear) [PITN-GA-2012-317521]
- EPSRC [EP/K020501/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K020501/1] Funding Source: researchfish
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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. (C) 2017 Acoustical Society of America.
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