Practical Prosody

Abstract

Prosody is clearly valuable for human understanding, but can be difficult to model in spoken language technology. This talk describes a “direct modeling” approach, which does not require any hand-labeling of prosodic events. Instead, prosodic features are extracted directly from the speech signal, based on time alignments from automatic speech recognition. Machine learning techniques then determine a prosodic model, and the model is integrated with lexical and other information to predict the target classes of interest. The talk presents a general method for prosodic feature extraction and design (including a special-purpose tool developed at SRI), and illustrates how it can be successfully applied in three different types of tasks: (1) detection of sentence or dialog act boundaries; (2) classification of emotion and affect, and (3) speaker classification.