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.

Impact and interest:

Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

44 since deposited on 11 Jan 2016
28 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 90074
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page