Detecting offensive user video blogs: an adaptive keyword spotting approach

Authors

M S. Barakat, University of WollongongFollow
C H. Ritz, University of WollongongFollow
David A. Stirling, University of WollongongFollow

RIS ID

76555

Publication Details

M. S. Barakat, C. H. Ritz & D. A. Stirling, "Detecting offensive user video blogs: an adaptive keyword spotting approach," in Third International Conference on Audio, Language and Image Processing, 2012, pp. 419-425.

Abstract

This paper proposes a speaker independent keyword spotting (KWS) approach applied to the audio track of user video blogs that can help in their automatic analysis, indexing, search and retrieval. The approach, which relies on matching of keyword templates to speech segments using an adaptive similarity threshold that is estimated automatically for each utterance, does not require training data or language model as required in existing approaches such as those based on the Hidden Markov Model (HMM). This is a particular advantage for user video blogs since they usually contain words of interest that have not been adequately represented in a training database. Experiments conducted to detect offensive words in video blogs achieved much higher accuracy than existing speech-to-text based approaches.