Lyan Verwimp

Analyzing the Contribution of Top-Down Lexical and Bottom-Up Acoustic Cues in the Detection of Sentence Prominence

Recent work has suggested that prominence perception could be driven by the predictability of the acoustic prosodic features of speech. On the other hand, lexical predictability and part of speech information are also known to correlate with prominence. In this paper, we investigate how the bottom-up acoustic and top-down lexical cues contribute to sentence prominence by using both types of features in unsupervised and supervised systems for automatic prominence detection. The study is conducted using a corpus of Dutch continuous speech with manually annotated prominence labels. Our results show that unpredictability of speech patterns is a consistent and important cue for prominence at both the lexical and acoustic levels, and also that lexical predictability and part-of-speech information can be used as efficient features in supervised prominence classifiers.

Language model adaptation for ASR of spoken translations using phrase-based translation models and named entity models

Language model adaptation based on Machine Translation (MT) is a recently proposed approach to improve the Automatic Speech Recognition (ASR) of spoken translations that does not suffer from a common problem in approaches based on rescoring i.e. errors made during recognition cannot be recovered by the MT system. In previous work we presented an efficient implementation for MT-based language model adaptation using a word-based translation model. By omitting renormalization and employing weighted updates, the implementation exhibited virtually no adaptation overhead, enabling its use in a real-time setting. In this paper we investigate whether we can improve recognition accuracy without sacrificing the achieved efficiency. More precisely, we investigate the effect of both state-of-the-art phrase-based translation models and named entity probability estimation. We report relative WER reductions of 6.2% over a word-based LM adaptation technique and 25.3% over an unadapted 3-gram baseline on an English-to-Dutch dataset.