Modeling the temporal dynamics of distinctive feature landmark detectors for speech recognition.
This paper elaborates on a computational model for speech recognition that is inspired by several interrelated strands of research in phonology, acoustic phonetics, speech perception, and neuroscience. The goals are twofold: (i) to explore frameworks for recognition that may provide a viable alternative to the current hidden Markov model (HMM) based speech recognition systems and (ii) to provide a computational platform that will facilitate engaging, quantifying, and testing various theories in the scientific traditions in phonetics, psychology, and neuroscience. This motivation leads to an approach that constructs a hierarchically structured point process representation based on distinctive feature landmark detectors and probabilistically integrates the firing patterns of these detectors to decode a phonological sequence. The accuracy of a broad class recognizer based on this framework is competitive with equivalent HMM-based systems. Various avenues for future development of the presented methodology are outlined.
Jansen
Aren
A
Department of Computer Science, University of Chicago, 1100 East 58th Street, Chicago, Illinois 60637, USA.
Niyogi
Partha
P
D001306:Auditory Pathways / Q000502:physiology*; D001309:Auditory Threshold; D003198:Computer Simulation; D003463:Cues; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D011571:Psychoacoustics; D017603:Signal Detection, Psychological; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D013067:Speech Perception; D049250:Speech Recognition Software; D013998:Time Perception