Improving word recognition in noise among hearing-impaired subjects with a single-channel cochlear noise-reduction algorithm

Abstract

A common complaint of the hearing impaired is the inability to understand speech in noisy environments even with their hearing assistive devices. Only a few single-channel algorithms have significantly improved speech intelligibility in noise for hearing-impaired listeners. The current study introduces a cochlear noise reduction algorithm. It is based on a cochlear representation of acoustic signals and real-time derivation of a binary speech mask. The contribution of the algorithm for enhancing word recognition in noise was evaluated on a group of 42 normal-hearing subjects, 35 hearing-aid users, 8 cochlear implant recipients, and 14 participants with bimodal devices. Recognition scores of Hebrew monosyllabic words embedded in Gaussian noise at several signal-to-noise ratios (SNRs) were obtained with processed and unprocessed signals. The algorithm was not effective among the normal-hearing participants. However, it yielded a significant improvement in some of the hearing-impaired subjects under different listening conditions. Its most impressive benefit appeared among cochlear implant recipients. More than 20% improvement in recognition score of noisy words was obtained by 12, 16, and 26 hearing-impaired at SNR of 30, 24, and 18 dB, respectively. The algorithm has a potential to improve speech intelligibility in background noise, yet further research is required to improve its performances.

An algorithm to improve speech recognition in noise for hearing-impaired listeners

“…An adapted version of this algorithm was later shown to improve speech intelligibility of cochlear implant (CI) users (Hu and Loizou, 2010). Recently, Fink et al (2012) tested a different binary-masking algorithm tailored specifically for white noise, and found a clear intelligibility benefit for CI users, but not for NH listeners or hearing-aid users.…”

Comparing Binaural Pre-processing Strategies I

“… Yousefian and Loizou (2012) , for example, supplemented their speech intelligibility evaluation with an instrumental evaluation of signal quality, while Healy et al. (2013) and Fink et al. (2012) additionally reported speech intelligibility improvements in hearing-impaired (HI) subjects.…”

“…Instrumental measures are commonly used to evaluate an algorithm's capabilities in speech intelligibility enhancement and quality improvement (e.g., . Perceptual speech intelligibility measurements in NH listeners (Fink, Furst, & Muchnik, 2012;Healy, Yoho, Wang, & Wang, 2013;Kim, Lu, Hu, & Loizou, 2009;Yousefian & Loizou, 2012) have also been used regularly to evaluate and characterize signal enhancement algorithms, often in combination with other measures. Yousefian and Loizou (2012), for example, supplemented their speech intelligibility evaluation with an instrumental evaluation of signal quality, while Healy et al (2013) and Fink et al (2012) additionally reported speech intelligibility improvements in hearing-impaired (HI) subjects.…”

“…Perceptual speech intelligibility measurements in NH listeners (Fink, Furst, & Muchnik, 2012;Healy, Yoho, Wang, & Wang, 2013;Kim, Lu, Hu, & Loizou, 2009;Yousefian & Loizou, 2012) have also been used regularly to evaluate and characterize signal enhancement algorithms, often in combination with other measures. Yousefian and Loizou (2012), for example, supplemented their speech intelligibility evaluation with an instrumental evaluation of signal quality, while Healy et al (2013) and Fink et al (2012) additionally reported speech intelligibility improvements in hearing-impaired (HI) subjects. Largescale evaluation studies in HI listeners (e.g., Cornelis et al, 2012;Luts et al, 2010) or CI users (e.g., Brockmeyer & Potts, 2011) have been geared toward comparing the value of different signal enhancement algorithms for the respective listener groups.…”

Comparing Binaural Pre-processing Strategies II

“…In general, data from speech intelligibility tests indicate that substantial improvements in speech intelligibility in noise can be achieved for CI users (unilateral as well as bilateral) by employing acoustic filtering such as binaural beamformers. Most tests of speech intelligibility, however, have been performed in artificial listening environments (often anechoic rooms) using stationary noise and partially colocated target speech and noise sources ( Fink, Furst, & Muchnik, 2012 ; Hehrmann, Fredelake, Hamacher, Dyballa, & Buechner, 2012 ; Hersbach et al., 2012 ; Kokkinakis & Loizou, 2010 ; Yang & Fu, 2005 ). Moreover, while numerous studies each assess a small number of algorithms, comparing the benefits for speech intelligibility across these studies is difficult because of differences in measurement procedures, stimulus characteristics (of the speech and noise), and differences between the groups of subjects assessed.…”