Syllable classification using static matrices and prosodic features

In this paper we explore the usefulness of prosodic features for syllable classification. In order to do this, we represent the syllable as a static analysis unit such that its acoustic-temporal dynamics could be merged into a set of features that the SVM classifier will consider as a whole. In the first part of our experiment we used MFCC as features for classification, obtaining a maximum accuracy of 86.66%. The second part of our study tests whether the prosodic information is complementary to the cepstral information for syllable classification. The results obtained show that combining the two types of information does improve the classification, but further analysis is necessary for a more successful combination of the two types of features

A Cross-media Retrieval System for Lecture Videos

We propose a cross-media lecture-on-demand system, in which users can selectively view specific segments of lecture videos by submitting text queries. Users can easily formulate queries by using the textbook associated with a target lecture, even if they cannot come up with effective keywords. Our system extracts the audio track from a target lecture video, generates a transcription by large vocabulary continuous speech recognition, and produces a text index. Experimental results showed that by adapting speech recognition to the topic of the lecture, the recognition accuracy increased and the retrieval accuracy was comparable with that obtained by human transcription