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Can spike-based speech recognition systems outperform conventional approaches ?

The field of automatic speech recognition (ASR) has advanced far enough in the past decade to produce numerous commercial applications such as the speech-driven telephone customer service menus now deployed by many companies. Unfortunately, these and other state-of-the-art ASR systems still pale in comparison to human performance, particularly in the presence of noise. Researchers have long been aware of this discrepancy in performance and have often turned to biology seeking clues to the robustness of the human auditory system. As a matter of fact, the most commonly employed features for ASR applications are still the Mel frequency cepstral coefficients (MFCC), which mimic the logarithmic distribution of channels throughout the frequency of hearing as observed in the cochlea. Nonetheless, today’s ASR systems are designed with a window-based mindset using Hidden Markov Models (HMMs) and have little resemblance to neurobiological computation. As is well known, neurons in the brain use all-or-nothing action potentials to communicate timing information. These spike trains code sensory inputs and all levels of processing throughout the brain. Rather than being artifacts of biology, we believe that spike trains provide a key to the wonderful noise robustness of the auditory system and can be exploited in man-made machine recognition systems. Recently, we proposed a spike-based classification scheme for simple acoustic signals that exploits the phase synchrony between the parallel streams of spike trains produced by the cochlea followed by a time-to-first-spike rank-order decoder for classification.1 A more recent version of our system replaces the rank-order decoder with a spiking neural network for improved classification. Comparisons with a typical ASR engine show improved performance under the presence of noise. According to Figure 1. Log-magnitude spectral envelope for /uh/ and the corresponding degree of phase synchrony for two sets of hair cells centered at 437Hz and 519Hz (computed for a noisy utterance with 5dB SNR).