Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
A specialized on-the-fly algorithm for lexicon and language model composition
IEEE Transactions on Audio, Speech, and Language Processing
Using finite state models for the integration of hierarchical LMs into ASR systems
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
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Finding the most likely sequence of symbols given a sequence of observations is a classical pattern recognition problem. This problem is frequently approached by means of the Viterbi algorithm, which aims at finding the most likely sequence of states within a trellis given a sequence of observations. Viterbi algorithm is widely used within the automatic speech recognition (ASR) framework to find the expected sequence of words given the acoustic utterance in spite of providing a suboptimal result. Word-graphs (WGs) are also frequently provided as the ASR output as a means of obtaining alternative hypotheses, hopefully more accurate than the one provided by the Viterbi algorithm. The trouble is that WGs can grow up in a very computationally inefficient manner. The aim of this work is to fully describe a specific method, computationally affordable, for getting a WG given the input utterance. The paper focuses specifically on the underlying approaches and their influence on both the spatial cost and the performance.