TR-2007-07
A Probabilistic Speech Recognition Framework Based on the Temporal
Dynamics of Distinctive Feature Landmark Detectors
Aren Jansen; Partha Niyogi. 1 June, 2007.
Communicated by Partha Niyogi.
Abstract
In this paper, we elaborate on a computational model for speech
recognition that is inspired by several different interrelated strands
of research in phonology, acoustic phonetics, speech perception, and
neuroscience. Our 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 (ii) to provide a
computational platform that will allow us to engage, quantify, and test
various theories in the scientific traditions in phonetics, psychology,
and neuroscience. Our approach uses the notion of distinctive features,
constructs a hierarchically structured point process representation
based on feature detectors, and probabilistically integrates the firing
patterns of these detectors to decode a phonetic sequence. We find the
accuracy of a broad class recognizer based on this framework to be
competitive with equivalent HMM-based systems. We conclude by outlining
various avenues for future development of our methodology.
Original Document
The original document is available in PDF (uploaded 1 June, 2007 by
Partha Niyogi).
