Impact of the Approaches Involved on Word-Graph Derivation from the ASR System
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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.
KeywordsLattice word-graphs automatic speech recognition
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