Prosodic cues for automatic phrase boundary detection in ASR

Abstract

This article presents a cross-lingual study for Hungarian and Finnish about the segmentation of continuous speech on word and phrasal level based on prosodie features. A word level segmenter has been developed which can indicate the word boundaries with acceptable accuracy for both languages. The ultimate aim is to increase the robustness of Automatic Speech Recognizers (ASR) by detection of word and phrase boundaries, and thus significantly decrease the searching space during the decoding process, very time-consuming in case of agglutinative languages, like Hungarian and Finnish. They are however fixed stressed languages, so by stress detection, word beginnings can be marked with reliable accuracy. An algorithm based on data-driven (HMM) approach was developed and evaluated. The best results were obtained by time series of fundamental frequency and energy together. Syllable length was found to be much less effective, hence was discarded. By use of supra-segmental features, word boundaries can be marked with high correctness ratio, if we allow not to find all of them. The method we evaluated is easily adaptable to other fixed-stress languages. To investigate this we adapted the method to the Finnish language and obtained similar results. © Springer-Verlag Berlin Heidelberg 2006.