Name
Abstract | ||
|---|---|---|
An extension to the conventional speech / nonspeech classification framework is presented for a scenario in which a number of microphones record the activity of speakers present at a meeting (one microphone per speaker). Since each microphone can receive speech from both the participant wearing the microphone (local speech) and other participants (crosstalk), the recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe a classifier in which a Gaussian mixture model (GMM) is used to model each class. A large set of potential acoustic features are considered, some of which have been employed in previous speech / nonspeech classifiers. A combination of two feature selection algorithms is used to identify the optimal feature set for each class. Results from the GMM classifier using the selected features are superior to those of a previously published approach. |
Year | Venue | Keywords |
|---|---|---|
2003 | European Conference on Speech Communication and Technology | crosstalk,feature selection,speech recognition,gaussian mixture model |
Field | DocType | Citations |
Pattern recognition,Feature selection,Voice activity detection,Audio mining,Computer science,Crosstalk,Speech recognition,Multi channel,Artificial intelligence,Classifier (linguistics),Microphone,Mixture model | Conference | 6 |
PageRank | References | Authors |
0.82 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stuart N. Wrigley | 1 | 181 | 20.56 |
| Guy J. Brown | 2 | 760 | 97.54 |
| Vincent Wan | 3 | 373 | 35.85 |
| Steve Renals | 4 | 2570 | 293.02 |