HVITE is a general-purpose Viterbi word recogniser. It will match a speech file against a network of HMMs and output a transcription in for each. When performing N-best recognition a word level lattice containing multiple hypotheses can also be produced.
Either a word level lattice or a label file is read in and then expanded using the supplied dictionary to create a model based network. This allows arbitrary finite state word networks and simple forced alignment to be specified.
This expansion can be used to create context independent, word internal context dependent and cross word context dependent networks. The way in which the expansion is performed is determined automatically from the dictionary and HMMList. When all labels appearing in the dictionary are defined in the HMMList no expansion of model names is performed. Otherwise if all the labels in the dictionary can be satisfied by models dependent only upon word internal context these will be used else cross word context expansion will be performed. These defaults can be overridden by HNET configuration parameters.
HVITE supports shared parameters and appropriately pre-computes output probabilities. For increased processing speed, HVITE can optionally perform a beam search controlled by a user specified threshold (see -t option). When fully tied mixture models are used, observation pruning is also provided (see the -c option).