It is well known that simple amplification cannot help many hearing-impaired listeners. As a consequence of this, numerous signal enhancement algorithms have been proposed for digital hearing aids. Many of these algorithms are only effective in certain environments. The ability to quickly and correctly detect elements of the auditory scene can permit the selection/parameterization of enhancement algorithms from a library of available routines. In this work, the authors examine the real time parameterization of a frequency-domain compression algorithm which preserves formant ratios and thus enhances speech understanding for some individuals with severe sensorineural hearing loss in the 2-3 kHz range. The optimal compression ratio is dependent upon qualities of the acoustical signal. We briefly review the frequency-compression technology and describe a Gaussian mixture model classifier which can dynamically set the frequency compression ratio according to broad acoustic categories which we call cohorts. We discuss the results of a prototype simulator which has been implemented on a general purpose computer.

Previous work by Merhav and Lee (1993) as well as others has emphasized that the conditions required to make the maximum a posteriori (MAP) decision rule an optimal decision rule for speech recognition do not hold and have proposed techniques based upon the adjustment of model parameters to improve speech recognition. In this article, we consider the problem of text-independent speaker recognition, and present a new model called the integral decode. The integral decode, like previous work in this area, attempts to compensate for the lack of conditions necessary to ensure optimality of the MAP decision rule in environments with corrupted observations and imperfect models. The integral decode is a smoothing operation in the feature space domain. A region of uncertainty is established about each noisy observation and an approximation of the integral is computed. The MAP decision rule is then applied to the smoothed likelihood estimates. In all tested conditions, the integral decode performs as well as or better than equivalent HMMs without integral decode.