title: Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners.
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.affiliation: Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
authors.forename: Jessica J M
authors.initials: JJ
authors.lastname: Monaghan
authors.affiliation: Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
authors.forename: Tobias
authors.initials: T
authors.lastname: Goehring
authors.affiliation: Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
authors.forename: Xin
authors.initials: X
authors.lastname: Yang
authors.affiliation: ExpORL, Katholieke Universiteit Leuven, Leuven, Belgium.
authors.forename: Federico
authors.initials: F
authors.lastname: Bolner
authors.affiliation: Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
authors.forename: Shangqiguo
authors.initials: S
authors.lastname: Wang
authors.affiliation: Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
authors.forename: Matthew C M
authors.initials: MC
authors.lastname: Wright
authors.affiliation: Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
authors.forename: Stefan
authors.initials: S
authors.lastname: Bleeck
mesh_terms: D000161:Acoustic Stimulation; D000368:Aged; D001302:Audiometry, Speech; D004558:Electric Stimulation; D005260:Female; D006310:Hearing Aids; D034381:Hearing Loss / Q000175:diagnosis / Q000523:psychology / Q000534:rehabilitation*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009622:Noise / Q000009:adverse effects*; D010470:Perceptual Masking; D019986:Persons with Hearing Disabilities / Q000523:psychology / Q000534:rehabilitation*; D021641:Recognition, Psychology; D012815:Signal Processing, Computer-Assisted; D013065:Speech Intelligibility; D013067:Speech Perception