A factor automaton approach for the forced alignment of long speech recordings

Abstract

This paper addresses the problem of aligning long speech recordings to their transcripts. Previous work has focused on using highly tuned language models trained on the transcripts to reduce the search space. In this paper we propose the use of a factor automaton, a well known method to represent all substrings from a string. This automaton encodes a highly constrained language model trained on the transcripts. We show competitive results with n-gram models in several testing scenarios. Preliminary experiments show perfect alignments at a reduced computational load and with a smaller memory footprint when compared to n-gram models.