This paper deals with the question of how the general public should be addressed when offering hearing screening. Postal-based questionnaires in the United Kingdom, Germany, and The Netherlands were sent to users of hearing devices, those that are in the process of obtaining one, or those that have indicated that they have special interest in hearing. Results of the survey indicated that respondents were enthusiastic about the idea of being able to carry out hearing self-screening tests via the internet, telephone, or questionnaires. A questionnaire as a method to screen on hearing was generally preferred above using the internet, which was preferred over using the telephone for the test. About 27% of the respondents indicated to use exclusively one method. Most respondents indicated that either method provided would be of interest (41%), 17% indicated not to be interested in conducting screening tests using the internet.
The present w ork concerns a system aimed at enhancing a target talker under varying signal conditions based on the use of several dierent t ypes of information or \cues". Toward this end, an architecture designed to combine separately operating estimators is described and evaluated. The architecture is currently implemented using spatial-and periodicitybased enhancement algorithms, and evaluated using a male target talker and female jammer talker under several spatial and target-to-jammer ratio (TJR) conditions. Using a TJR estimation algorithm, the implementation is shown to yield improved TJR under all tested input TJRs (-4, 0, 4, and 8 dB) and spatial conditions (target and jammer straight ahead; target ahead and jammer at 60 degrees). Improvement ranges from 1.4 to 4.5 dB.
A major deficiency in state-of-the-art automatic speech recognition systems is the lack of robustness in additive and convolutive noise. The model of auditory perception, as developed by Dau et al. [J. Acoust. Soc. Am. 99, 3615–3622 (1996)] for psychoacoustical purposes, partly overcomes these difficulties when used as a front-end for speech recognition. Especially in combination with locally-recurrent neural networks (LRNN) the model output, called ‘‘internal representation’’ had been shown to provide highly robust feature vectors [Tchorz and Kollmeier, J. Acoust. Soc. Am. (submitted)]. To further improve the performance of this auditory-based LRNN recognition system in background noise, different speech enhancement methods were examined. The minimum mean-square error (MMSE) short-term spectral amplitude estimator (STSA), as proposed by Ephraim and Malah [IEEE Trans. Acoust., Speech, Signal Process. 32, 1109–1121 (1984)], was compared to a binaural Wiener filter [Wittkop et al., this meeting], based on directional and coherence cues. Both noise reduction algorithms yield highly improved recognition rates in nonreverberant noisy conditions, while the performance in clean speech is not significantly affected. The algorithms were also evaluated in real-world reverberant conditions with speech-simulating noise and jammer speech.
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