Using prosody in fixed stress languages for improvement of speech recognition

In this chapter we examine the usage of prosodic features in speech recognition, with a special attention payed to agglutinating and fixed stress languages. Current knowledge in speech prosody exploitation is addressed in the introduction. The used prosodic features, acoustic-prosodic pre-processing, and segmentation in terms of prosodic units are presented in details. We use the expression "prosodic unit" in order to differentiate them from prosodic phrases, which are usually longer. We trained a HMM-based prosodic segmenter relying on fundamental frequency and intensity of speech. The output of this prosodic segmenter is used for N-best lattice rescoring in parallel with a simplified bigram language model in a continuous speech recognizer, in order to improve speech recognition performance. Experiments for Hungarian language show a WER reduction of about 4% using a simple lattice rescoring. The performance of the prosodic segmenter is also investigated in comparison with our earlier experiments.