This thesis presents methods for supervised and unsupervised speaker adaptation of Gaussian mixture speaker models in text-independent speaker verification. The proposed methods are based on an approach which is able to separate speaker and channel variability so that progressive updating of speaker models can be performed while minimizing the influence of the channel variability associated with the adaptation recordings. This approach relies on a joint factor analysis model of intrinsic speaker variability and session variability where inter-session variation is assumed to result primarily from the effects of the transmission channel. These adaptation methods have been evaluated under the adaptation paradigm defined under the NIST 2005 speaker recognition evaluation plan which is based on conversational telephone speech.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.100735 |
| Date | January 2006 |
| Creators | Shou-Chun, Yin, 1980- |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Engineering (Department of Electrical and Computer Engineering.) |
| Rights | © Yin Shou-Chun, 2006 |
| Relation | alephsysno: 002603225, proquestno: AAIMR32628, Theses scanned by UMI/ProQuest. |
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