Application of sequential decoding for converting phonetic to graphic representation in automatic recognition of continuous speech(ARCS)

Abstract

Following segmentation and phonetic classification in automatic recognition of continuous speech (ARCS), it is necessary to provide methods for linguistic decoding, In this work a graph search procedure, based on the Fano algorithm, is used to convert machine-contaminated phonetic descriptions of speaker performance into standard orthography. The information utilized by the decoder consists of a syntax, a lexicon containing transcription variation for each word, and performance-based statistics from acoustic analysis. The latter contain information related to automatic segmentation and classification accuracy and certainty (anchor-point) data. A distinction is made between speaker- and machine-dependent corruption of phonetic input strings. Preliminary results are presented and discussed, together with some considerations for evaluation.