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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rossi, M. Amft, O. Kusserow, M. Troster, G. |
| Copyright Year | 2010 |
| Description | Author affiliation: Signal Processing Systems, TU Eindhoven, The Netherlands (Amft, O.) || Wearable Computing Lab., ETH Zurich, Switzerland (Rossi, M.; Kusserow, M.; Troster, G.) |
| Abstract | We present an unsupervised speaker identification system for personal annotations of conversations and meetings. The system dynamically learns new speakers and recognizes already known speakers using one audio channel and speech-independent modeling. Multiple personal systems could collaborate in robust unsupervised speaker identification and online learning. The system was optimized for real-time operation on a DSP system that can be worn during daily activities. The system was evaluated on the freely available 24-speaker Augmented Multiparty Interaction dataset. For 5 s recognition time, the system achieves 81% recognition rate. Collaboration between four identification systems resulted in a performance increase of up to 17%, however even two collaborating systems yield an performance improvement. A prototypical wearable DSP implementation could continuously operate for more than 8 hours from a 4.1 Ah battery. |
| Starting Page | 180 |
| Ending Page | 189 |
| File Size | 1099987 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781424453290 |
| e-ISBN | 9781424453283 |
| DOI | 10.1109/PERCOM.2010.5466976 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-03-29 |
| Publisher Place | Germany |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Collaboration Real time systems Collaborative work Wearable computers Digital signal processing World Wide Web Speech recognition Robustness Prototypes Speech analysis |
| Content Type | Text |
| Resource Type | Article |
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