Example Based Large Vocabulary Speech Recognition

March 23, 2010
Dirk Van Compernolle | K.U.Leuven – ESAT

Research into Example Based Speech Recognition got revived over the past decade.
Example based recognition is appealing for a number of reasons: there is considerable evidence from the linguistic literature that humans store actual traces of at least some sentences or phrases. Moreover, after 40 years of refining the HMM framework, we are still stuck with a model that is fundamentally flawed in a number of manners, most of the all the first order Markov assumption. Example based recognition avoids some of the traps of HMMs: (i) the data is not compacted into suboptimal models; (ii) all the data – with all its detail – is available at the moment of recognition. In this talk we will address besides the global framework some of the major challenges that we encountered. The example based approach is by and large the non-parametric statistical counterpart of the parametric HMMs.
In this process we were confronted with some issues less prominent in HMMs distance metrics, outliers, merit of individual data points, distinguishing ‘outright wrong’ vs. ‘unusual but correct’, … For some of these we came up with novel and interesting solutions, for others we surely don’t have a definite answer. Also, for some of these, our interpretation had to be revised as we are moving to increasingly large databases.

Speaker Details

Dirk Van Compernolle received his Ph.D. from Stanford University in 1985 with a doctorate on speech signal processing for cochlear implants. From 1985 till 1987 he was at the IBM Research working on robust speech recognition. In 1987 he joined the K.U.Leuven, Belgium, where he held various positions and where he has been a professor since 1994. From 1994 till 1998 he was a Vice President at Lernout and Hauspie Speech Products, responsible for the speech recognition and basic research divisions. His research interests include robust speech recognition, speech enhancement, microphone arrays, novel speech recognition paradigms. His main activities lately have been in the development of large vocabulary example based speech recognition.