Environment-aware Listener-Optimized Binaural Enhancement of Speech (E-LOBES)

Age-related hearing loss affects over half the UK population aged over 60. Hearing loss makes communication difficult and so has severe negative consequences for quality of life. The most common treatment for mild-to-moderate hearing loss is the use of hearing aids. However even with aids, hearing impaired listeners are worse at understanding speech in noisy environments because their auditory system is less good at separating wanted speech from unwanted noise. One solution for this is to use speech enhancement algorithms to amplify the desired speech signals selectively while attenuating the unwanted background noise.

It is well known that normal hearing listeners can better understand speech in noise when listening with two ears rather than with only one. Differences between the signals at the two ears allow the speech and noise to be separated based on their spatial locations resulting in improved intelligibility. Technological advances now make feasible the use of two hearing aids that are able to share information via a wireless link. By sharing information in this way, it becomes possible for the speech enhancement algorithms within the hearing aids to localize sound sources more accurately and, by jointly processing the signals for both ears, to ensure that the spatial cues that are present in the acoustic signals are retained. It is the goal of this project to exploit these binaural advantages by developing speech enhancement algorithms that jointly enhance the speech received by the two ears.

Most current speech enhancement techniques have evolved from the telecommunications industry and are designed to act only on monaural signals. Many of the techniques can improve the perceived quality of already intelligible speech but binary masking is one of the few techniques that has been shown to improve the intelligibility of noisy speech for both normal and hearing impaired listeners. In the binary masking approach regions of the time-frequency domain that contain significant speech energy are left unchanged while regions that contain little speech energy are muted. In this project we will extend existing monaural binary masking techniques to provide binaural speech enhancement while preserving the inter-aural time and level differences that are critical for the spatial separation of sound sources.

To train and tune our binaural speech enhancement algorithm we will also develop within the project an intelligibility metric that is able to predict the intelligibility of a speech signal for a binaural listener with normal or impaired hearing in the presence of competing noise sources. This metric is the key to finding automatically the optimum settings an individual listener's hearing aids in a particular environment.

The final evaluation and development of the binaural enhancement algorithm assess speech perception in noise in a panel of hearing-impaired listeners who will also be asked to assess the quality of the enhanced speech signals.

The immediate beneficiaries from this research will be hearing aid manufacturers and, through them, hearing aid users. We expect that the techniques and algorithms developed in this research will make it substantially easier for hearing aid users to distinguish the individual speakers that are present especially when in a noisy listening environment.

However there are other circumstances in which listeners must use headphones or earphones when operating in an environment with high ambient noise. The techniques developed here can also benefit such users by allowing them to communicate intelligibly with others in a noisy environment.

The project will develop methods of characterizing the listening context and the acoustic properties of the listening environment from the received microphone signals and, from this information, identifying the listening context automatically. This is potentially of considerable benefit to designers of mobile phones or other mobile devices who will be able to adapt the operation of their systems to the context in which it is being used.