title: Noise-robust speech recognition through auditory feature detection and spike sequence decoding.
abstract: Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.
authors: Department of Physics and Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802, U.S.A. pbs130@psu.edu.
authors: Phillip B
authors: PB
authors: Schafer
authors: Dezhe Z
authors: DZ
authors: Jin
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D009622:Noise; D010363:Pattern Recognition, Automated; D059629:Signal-To-Noise Ratio; D013018:Sound Spectrography; D013060:Speech; D013061:Speech Acoustics; D049250:Speech Recognition Software; D013997:Time Factors; D014825:Vocabulary