Abstract We present a detailed experimental study of Gaussian splitting and merging algorithms to train the parameters of state-clustered hidden Markov model (HMM) automatic speech recognition (ASR) systems. Gaussian splitting uniformly distributes the training data[…]
Abstract Hidden Markov Models (HMMs) were well established in the late eighties during the height of the Resource Management evaluations. They have been so successful that they form the basis of virtually all speech recognition[…]
Abstract We are exploring the application of relational learning methods, such as inductive logic programming, to the construction of natural language processing systems. We have developed a system, CHILL, for learning a deterministic parser from[…]
Abstract Within the framework of Articulatory Phonology (e.g., Browman & Goldstein, 1992), the primitive units of phonological structure are hypothesized to be articulatory gestures. Gestures are abstract characterizations of vocal tract constriction actions and are[…]
Abstract Finding simple, non-recursive, base noun phrases is an important subtask for many natural language processing applications. While previous empirical methods for base NP identification have been rather complex, this talk instead propose a very[…]
Abstract Although the intelligibility of the acoustic speech signal is usually very high, in many situations speech reception is improved if cues derived from the visible actions of the talker’s face are integrated with cues[…]
Abstract Robust speech processing poses one of the greatest challenges to the speech research community. Speech recognizes and speech coders are particularly sensitive to channel mismatch. The primary goal of robust speech processing is to[…]
