Attention, intentions, and the structure of discourse
Computational Linguistics
A Cache-Based Natural Language Model for Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document and passage retrieval based on hidden Markov models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
FILOCHAT: handwritten notes provide access to recorded conversations
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
“I'll get that off the audio”: a case study of salvaging multimedia meeting records
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Document expansion for speech retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
ECDL '97 Proceedings of the First European Conference on Research and Advanced Technology for Digital Libraries
Topic segmentation: algorithms and applications
Topic segmentation: algorithms and applications
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The reliability of a dialogue structure coding scheme
Computational Linguistics
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Discourse segmentation by human and automated means
Computational Linguistics
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Mixed initiative in dialogue: an investigation into discourse segmentation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
DiaSumm: flexible summarization of spontaneous dialogues in unrestricted domains
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Activity detection for information access to oral communication
HLT '01 Proceedings of the first international conference on Human language technology research
HMM and neural network based speech act detection
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Digesting virtual "geek" culture: the summarization of technical internet relay chats
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Scalable summaries of spoken conversations
Proceedings of the 13th international conference on Intelligent user interfaces
Inter-coder agreement for computational linguistics
Computational Linguistics
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Topical segmentation is a basic tool for information access to audio records of meetings and other types of speech documents which may be fairly long and contain multiple topics. Standard segmentation algorithms are typically based on keywords, pitch contours or pauses. This work demonstrates that speaker initiative and style may be used as segmentation criteria as well. A probabilistic segmentation procedure is presented which allows the integration and modeling of these features in a clean framework with good results.Keyword based segmentation methods degrade significantly on our meeting database when speech recognizer transcripts are used instead of manual transcripts. Speaker initiative is an interesting feature since it delivers good segmentations and should be easy to obtain from the audio. Speech style variation at the beginning, middle and end of topics may also be exploited for topical segmentation and would not require the detection of rare keywords.