Broadcast News (BN) speech recognition transcription has attracted research due to the challenges of the task since the mid 1990's. More recently, research has been moving towards more spontaneous broadcast data, commonly called Broadcast Conversation (BC) speech. Considering the large style difference between BN and BC genres, specific modeling of genres should intuitively result in improved system performance. In this paper BN- and BC-style speech recognition has been explored by designing genre-specific systems. In order to separate the training data, an automatic genre categorization with two novel features is proposed. Experiments showed that automatic categorization of genre labels of the training data compared favorably to the original manually specified genre labels provided with corpora. When test data sets were classified into BN or BC genres and tested by the corresponding genre-specific speech recognition systems, modest but consistent error reductions were achieved compared to the baseline genreindependent systems.
Bibliographic reference. Zhang, Qingqing / Lamel, Lori / Gauvain, Jean-Luc (2011): "Genre categorization and modeling for broadcast speech transcription", In INTERSPEECH-2011, 1709-1712.