Quantifying temporal speech reduction in French using forced speech alignment

Abstract

The processing of speech reduction remains one of the major challenges to automatic speech recognition (ASR) systems, as speech reduction often results in a considerable number of automatic transcription errors. Conversely, automatic transcription errors may indicate interesting reduction phenomena. The present article focuses on temporal speech reduction in spoken French. In a series of explorations, we examined large speech corpora with respect to production variation in different speech styles, and in particular, to shorter pronunciations and disappearing sounds. An ASR tool was used, forced speech alignment, that allows one to locate and to quantify speech regions prone to temporal reductions. Our study made use of various styles of large speech corpora, including broadcast news, as well as telephone and face-to-face conversations, thereby including both casual and careful speech. The results highlight the increasing impact of temporal speech reduction with less formal, more spontaneous and interactive speaking styles. In a broader sense, our study provides a demonstration of how ASR systems can be employed to consistently explore variations in speech in virtually unlimited speech corpora.