Automatic Genre and Show Identification of Broadcast Media
Table 4: Genre/show classification accuracy using text based LDA models
['#Domains', 'Subtitles Genre ID', 'Subtitles Show ID', 'ASR Output Genre ID', 'ASR Output Show ID']
Transcripts of the shows have valuable information for discrimination of genres and shows. In this section the classification is studied based on solely textual features. BBC TVs provide subtitles of the TV soundtrack, mostly for helping deaf and hard-of-hearing viewers. The quality of these subtitles varies considerably by genres. For example subtitles of live events and news are mostly re-spoken live ASR output and have higher errors, however for other genres which does not have the live nature, the quality is higher. Subtitles were used as-is, without any preprocessing, to train the classifiers for both tasks. Although subtitles can be of varied quality, their correctness is still high. In a second experiment, ASR output is used instead of subtitles. The ASR systems used here were trained for participation in the MGB Challenge. The classification task here is similar to a document classification task, where each show’s transcript is a document and the classes are either genres or shows. To have a fair comparison with the acoustic LDA experiments, text based LDA models were trained and the domain posteriors were used as features in the SVM classifiers. A simpler approach would be SVMs with tf-idf features directly. LDA models trained with the subtitles performed substantially better than models trained on the ASR output. Note that the ASR models used here have around 30% WER on the official development set of the MGB challenge. The performance gap is even wider in case of the show ID task, 22.6% vs. 13.5% absolute difference. This could caused by some specific names that were present in the subtitles, but not in the ASR output. Such words may have considerable discriminability information.