Detection and identification of speech sounds using cortical activity patterns

Abstract

We have developed a classifier capable of locating and identifying speech sounds using activity from rat auditory cortex with an accuracy equivalent to behavioral performance and without the need to specify the onset time of the speech sounds. This classifier can identify speech sounds from a large speech set within 40ms of stimulus presentation. To compare the temporal limits of the classifier to behavior, we developed a novel task that requires rats to identify individual consonant sounds from a stream of distracter consonants. The classifier successfully predicted the ability of rats to accurately identify speech sounds for syllable presentation rates up to 10 syllables per second (up to 17.9±1.5bits/s), which is comparable to human performance. Our results demonstrate that the spatiotemporal patterns generated in primary auditory cortex can be used to quickly and accurately identify consonant sounds from a continuous speech stream without prior knowledge of the stimulus onset times. Improved understanding of the neural mechanisms that support robust speech processing in difficult listening conditions could improve the identification and treatment of a variety of speech-processing disorders.