Title :
Transcription-based video genre classification
Author :
Oger, Stanislas ; Rouvier, Mickael ; Linares, Georges
Author_Institution :
CERI-LIA, Univ. of Avignon, Avignon, France
Abstract :
In this paper, we present a new method for video genre identification based on the linguistic content analysis. This approach relies on the analysis of the most frequent words in the video transcriptions provided by an automatic speech recognition system. Experiments are conducted on a corpus composed of cartoons, movies, news, commercials, documentary, sport and music. On this 7-genre identification task, the proposed transcription-based method obtains up to 80% of correct identification. Finally, this rate is increased to 95% by combining the proposed linguistic-level features with low-level acoustic features.
Keywords :
speech recognition; video retrieval; automatic speech recognition system; linguistic content analysis; linguistic-level features; low-level acoustic features; transcription-based video genre classification; Automatic speech recognition; Data mining; Feature extraction; Frequency; Indexing; Internet; Motion pictures; Speech analysis; Support vector machine classification; Support vector machines; audio-based video processing; linguistic feature extraction; video genre classification;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495042
