Topic dependent language modelling for spoken term detection

Kalantari, Shahram, Dean, David, & Sridharan, Sridha (2014) Topic dependent language modelling for spoken term detection. In Europian Signal Processing Conference (EUSIPCO 2014), 1-5 September 2014, Lisbon, Portugal. (Unpublished)


This paper investigates the effect of topic dependent language models (TDLM) on phonetic spoken term detection (STD) using dynamic match lattice spotting (DMLS). Phonetic STD consists of two steps: indexing and search. The accuracy of indexing audio segments into phone sequences using phone recognition methods directly affects the accuracy of the final STD system. If the topic of a document in known, recognizing the spoken words and indexing them to an intermediate representation is an easier task and consequently, detecting a search word in it will be more accurate and robust. In this paper, we propose the use of TDLMs in the indexing stage to improve the accuracy of STD in situations where the topic of the audio document is known in advance. It is shown that using TDLMs instead of the traditional general language model (GLM) improves STD performance according to figure of merit (FOM) criteria.

Impact and interest:

2 citations in Scopus
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ID Code: 75760
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Spoken term detection, Language modelling, Audio indexing
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > OTHER INFORMATION AND COMPUTING SCIENCES (089900)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Research Centres > Smart Services CRC
Copyright Owner: Copyright 2014 The Author(s)
Deposited On: 28 Aug 2014 23:30
Last Modified: 10 May 2015 00:30

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