title: Large-scale training to increase speech intelligibility for hearing-impaired listeners in novel noises.
abstract: Supervised speech segregation has been recently shown to improve human speech intelligibility in noise, when trained and tested on similar noises. However, a major challenge involves the ability to generalize to entirely novel noises. Such generalization would enable hearing aid and cochlear implant users to improve speech intelligibility in unknown noisy environments. This challenge is addressed in the current study through large-scale training. Specifically, a deep neural network (DNN) was trained on 10 000 noises to estimate the ideal ratio mask, and then employed to separate sentences from completely new noises (cafeteria and babble) at several signal-to-noise ratios (SNRs). Although the DNN was trained at the fixed SNR of - 2 dB, testing using hearing-impaired listeners demonstrated that speech intelligibility increased substantially following speech segregation using the novel noises and unmatched SNR conditions of 0 dB and 5 dB. Sentence intelligibility benefit was also observed for normal-hearing listeners in most noisy conditions. The results indicate that DNN-based supervised speech segregation with large-scale training is a very promising approach for generalization to new acoustic environments.
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Jitong
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Yuxuan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Speech and Hearing Science, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Sarah E
authors.initials: SE
authors.lastname: Yoho
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: DeLiang
authors.initials: D
authors.lastname: Wang
authors.affiliation: Department of Speech and Hearing Science, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Eric W
authors.initials: EW
authors.lastname: Healy
mesh_terms: D000161:Acoustic Stimulation; D000328:Adult; D000368:Aged; D000465:Algorithms; D001309:Auditory Threshold; D016022:Case-Control Studies; D004558:Electric Stimulation; D005260:Female; D006310:Hearing Aids; D006319:Hearing Loss, Sensorineural / Q000175:diagnosis / Q000503:physiopathology / Q000523:psychology / Q000534:rehabilitation*; D006801:Humans; 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*; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D013065:Speech Intelligibility; D013067:Speech Perception; D055815:Young Adult