An algorithm to increase speech intelligibility for hearing-impaired listeners in novel segments of the same noise type

Citations Over TimeTop 10% of 2015 papers

Abstract

Machine learning algorithms to segregate speech from background noise hold considerable promise for alleviating limitations associated with hearing impairment. One of the most important considerations for implementing these algorithms into devices such as hearing aids and cochlear implants involves their ability to generalize to conditions not employed during the training stage. A major challenge involves the generalization to novel noise segments. In the current study, sentences were segregated from multi-talker babble and from cafeteria noise using an algorithm that employs deep neural networks to estimate the ideal ratio mask. Importantly, the algorithm was trained on segments of noise and tested using entirely novel segments of the same nonstationary noise type. Substantial sentence-intelligibility benefit was observed for hearing-impaired listeners in both noise types, despite the use of unseen noise segments during the test stage. Interestingly, normal-hearing listeners displayed benefit in babble but not in cafeteria noise. This result highlights the importance of evaluating these algorithms not only in human subjects, but in members of the actual target population.

Related Papers

• → Towards the improvement of automatic recognition of dysarthric speech(2009)29 cited
• → Polish sentence tests for measuring the intelligibility of speech in interfering noise(2009)46 cited
• → Speech enhancement models suited for speech recognition using composite source and wavelet decomposition model(2010)1 cited
• SPEECH ENHANCEMENT USING SPECTRAL SUBTRACTION FOR NOISE-SUPPRESSED SPEECHSIGNAL(2020)
• A measurement method to discriminate aircraft fly-over noise(2010)