Improving spoken term detection using complementary information
Kalantari, Shahram (2015) Improving spoken term detection using complementary information. PhD thesis, Queensland University of Technology.
This research has made contributions to the area of spoken term detection (STD), defined as the process of finding all occurrences of a specified search term in a large collection of speech segments. The use of visual information in the form of lip movements of the speaker in addition to audio and the use of topic of the speech segments, and the expected frequency of words in the target speech domain, are proposed. By using these complementary information, improvement in the performance of STD has been achieved which enables efficient search of key words in large collection of multimedia documents.
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Sridharan, Sridha, Tjondronegoro, Dian, & Dean, David|
|Keywords:||spoken term detection, Multimedia indexing, Audio visual speech recognition, Dynamic match lattice spotting, Synchronous hidden Markov model, cross database training, Fused HMM adaptation, HMM adaptation, Phone recognition|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Institution:||Queensland University of Technology|
|Deposited On:||11 Jan 2016 02:57|
|Last Modified:||11 Jan 2016 02:58|
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