BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
The Rules Behind Roles: Identifying Speaker Role in Radio Broadcasts
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The kappa statistic: a second look
Computational Linguistics
Topic and speaker identification via large vocabulary continuous speech recognition
HLT '93 Proceedings of the workshop on Human Language Technology
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Automatic named identification of speakers using diarization and ASR systems
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Initial study on automatic identification of speaker role in broadcast news speech
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
IEEE Transactions on Multimedia
Speaker role recognition to help spontaneous conversational speech detection
Proceedings of the 2010 international workshop on Searching spontaneous conversational speech
Automatic indexing of speech segments with spontaneity levels on large audio database
Proceedings of the 2010 international workshop on Searching spontaneous conversational speech
Analysis and automatic recognition of false starts in spontaneous speech
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Enriching speech recognition with automatic detection of sentence boundaries and disfluencies
IEEE Transactions on Audio, Speech, and Language Processing
Recognizing disfluencies in conversational speech
IEEE Transactions on Audio, Speech, and Language Processing
Edit disfluency detection and correction using a cleanup language model and an alignment model
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Multimedia
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Processing spontaneous speech is one of the many challenges that automatic speech recognition systems have to deal with. The main characteristics of this kind of speech are disfluencies (filled pause, repetition, false start, etc.) and many studies have focused on their detection and correction. Spontaneous speech is defined in opposition to prepared speech, where utterances contain well-formed sentences close to those found in written documents. Acoustic and linguistic features made available by the use of an automatic speech recognition system are proposed to characterize and detect spontaneous speech segments from large audio databases. To better define this notion of spontaneous speech, segments of an 11-hour corpus (French Broadcast News) had been manually labeled according to three classes of spontaneity. Firstly, we present a study of these features. We then propose a two-level strategy to automatically assign a class of spontaneity to each speech segment. The proposed system reaches a 73.0% precision and a 73.5% recall on high spontaneous speech segments, and a 66.8% precision and a 69.6% recall on prepared speech segments. A quantitative study shows that the classes of spontaneity are useful information to characterize the speaker roles. This is confirmed by extending the speech spontaneity characterization approach to build an efficient automatic speaker role recognition system.