Perceptual Methods for Speech Communication

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Speech communication is arguably the most important social activity. Speech signals are packed with verbal and non-verbal information. Humans can learn a great deal from listening to just a snippet of spoken speech: the spoken words and language; the speaker's identity, gender, accent, age, language proficiency, emotional state, health; the background environment based on the ambient sounds and noises captured; and any communications/recording media which carried the signal, based on perceivable “signatures” imprinted by the media on the signal. Moreover, human listeners are remarkably capable of comprehending the embedded linguistic and paralinguistic information even when the speech signal is highly corrupted. Computational intelligence, as embodied in the signal processing algorithms that run on electronic devices, is not (yet) a match for human performance. Nevertheless, the emerging Internet of Everything will further multiply the opportunities for speech communication between humans and electronic devices and between humans mediated by networked devices. Such speech communication can happen anytime and anywhere. As a result, the instances of devices picking up degraded speech signals will mushroom. To address this challenge, we propose to research speech signal processing methods that would enable machine extraction of speech information to be resilient to acoustic degradations, i.e., ambient noise and reverberation. Ideas for innovation will draw upon emerging advances in speech cognitive science and robust signal modeling and recovery techniques. New speech signal processing methods inspired by human cortical processing of acoustic speech signals will be developed. Moreover, a speech signal model ubiquitously deployed in today's cellular telephones will be revamped. A major goal is to advance the current level of machine comprehension of verbal information embedded in degraded speech signals, and by doing so also provide human listeners with highly intelligible speech. The new knowledge, methods, and algorithms anticipated from the proposed research will engender new capabilities for enhancing existing and enabling future speech communication and acoustic interface technologies. Besides training highly qualified R&D personnel, the research will produce new and better tools to benefit both industry and academia. One type of tool to be produced will enable R&D engineers to optimize their speech enhancement algorithms. Industry sectors that will benefit from the proposed research include communications and information technology, gaming, machine-mediated learning, hearing instruments, health and fitness, and robotics. The new knowledge and solutions are anticipated to also contribute to farther fields such as soundscape design for indoor and outdoor environments.