Parsing speech into articulatory events

In this paper, the states in the speech production process are defined by a number of categorical articulatory features. We describe a detector that outputs a stream (sequence of classes) for each articulatory feature given the Mel frequency cepstral coefficient (MFCC) representation of the input speech. The detector consists of a bank of recurrent neural network (RNN) classifiers, a variable depth lattice generator and Viterbi decoder. A bank of classifiers has been previously used for articulatory feature detection by many researchers. We extend their work first by creating variable depth lattices for each feature and then by combining them into product lattices for rescoring using the Viterbi algorithm. During the rescoring we incorporate language and duration constraints along with the posterior probabilities of classes provided by the RNN classifiers. We present our results for the place and manner features using TIMIT data, and compare the results to a baseline system. We report performance improvements both at the frame and segment levels.