In this paper we derive algorithms that construct simple continuous-density hidden Markov models based on unlabeled observations of user actions. These models are then be used to predict future actions of the user. The algorithms were designed for domains where user-generated training data are costly to obtain and, consequently, the number of free parameters must be kept low. More specifically,...

The most successful modeling approach to automatic speech recognition (ASR) is to use a set of hidden Markov models (HMMs) as the acoustic models for subword or whole-word speech units and to use the statistical N-gram model as language model for words and/or word classes in sentences. All the model parameters, including HMMs and N-gram models, are estimated from a large amount of training data...

LARGE MARGIN TRAINING OF ACOUSTIC MODELS FOR SPEECH RECOGNITION Fei Sha Advisor: Prof. Lawrence K. Saul Automatic speech recognition (ASR) depends critically on building acoustic models for linguistic units. These acoustic models usually take the form of continuous-density hidden Markov models (CD-HMMs), whose parameters are obtained by maximum likelihood estimation. Recently, however, there ha...