Development of innovative speech enhancement algorithms based on the central auditory system.

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Multimedia devices such as tablets, smart phones and now smart watches and glasses are commonly used in noisy environments by millions of Canadians. These devices include many speech processing algorithms, such as speech coders or automatic speech recognizers (ASR), whose performances are seriously affected by the presence of noise. For example, an ASR can identify 85% of the words correctly in a noise-free environment; however, this percentage can drop to 31% with a signal-to-noise ratio (SNR) of 10 dB. In order to limit this decrease in performance, speech enhancement (SE) modules, which aim at reducing the noise level without affecting the speech quality, are included in these devices. The performance of these SE modules is largely sub-optimal. In fact, a study having compared 14 of the best SE algorithms reports a maximum subjective score of barely 3/5 for an SNR of 10 dB. In sharp contrast, the auditory system deals very well with noise. In fact, it is fairly easy for humans to follow a conversation in a relatively noisy environment. **The long-term objective (+ 10 years) of my research program is thus to develop commercially viable SE algorithms that are inspired by the central auditory system, i.e. the part of the auditory system between the auditory nerve and auditory cortex, with the goal of approaching the excellent performance of the auditory system in the presence of noise. In the short-term (less than 5 years), the main objectives will be to statistically model the representation of noisy vocalizations by the neurons of the central auditory system as well as to develop SE algorithms based on these models. To achieve these short-term objectives, we will first represent neural discharges as point processes and use this representation to develop statistical models of neural coding and decoding; neural coding being the estimation of a spike train given a stimulus, such as a vocalization, and neural decoding, the estimation of a stimulus given a spike train. These models will specifically take into account the presence of noise in vocalizations. Furthermore, we will use the derived models to develop statistical estimators for SE. Since these statistical estimators will be set in a domain closer to the one of the central auditory system, we expect the resulting estimators to be more perceptually relevant and thus more efficient. **The recent development of accurate, yet simple, statistical models of neural signals opens a promising research avenue for SE that will be exploited in the current proposal. Moreover, this proposal will allow for the training of multidisciplinary researchers having skills in neuroscience, statistical signal processing and speech processing. Upon completion, this program will improve the performance of SE modules and will therefore allow a much more efficient use of millions of multimedia portable devices such as tablets, smart phones, watches or glasses.