Text-to-speech alignment of long recordings using universal phone models

Abstract

Large recordings like radio broadcasts or audio books are an interesting additional resource for speech research but often have nothing but an orthographic transcription available in terms of annotation. The alignment between text and speech is an important first step for further processing. Conventionally, this is done by using automatic speech recognition (ASR) on the speech corpus and then aligning recognition result and transcription. This has the drawback that an ASR system needs to be available for the target language. In this paper, we introduce an approach based on forced alignment with hidden Markov models (HMM) normally applied only to shorter utterances. We show that by using a set of generalized phone models computed over phonetic groups, forced alignment is able to reliably align text and speech while being robust against transcription errors. In contrast to ASR methods, the alignment models can be used in a language-independent way. Index Terms: speech corpora annotation, sentence segmentation, large corpora processing