Detection of confusable words in automatic speech recognition

Abstract

A new method to detect words that are likely to be confused by speech recognition systems is presented in this letter. A new dissimilarity measure between two words is calculated in two steps. First, the phonetic transcriptions of the words are aligned using only phonetic information. Two kinds of alignments are used: either with or without insertions and deletions. Second, the dissimilarity measure is calculated on the basis of the resulting alignment and acoustic information obtained from the hidden Markov models of the phones. In a classical false acceptance/false rejection framework, the equal error rate was measured to be less than 5%.