Partial matching and search space reduction for QbE-STD. (September 2017)

• Record Type: Journal Article
• Authors: Madhavi, Maulik C.; Patil, Hemant A.
• Is Part Of: Computer speech & language. Volume 45(2017)
• Page Start: 58
• Page End: 82
• Publication Date: 2017-09
• Subjects: Query-by-Example Spoken Term Detection -- Dynamic time warping -- Non-exact DTW matching -- Phonetic posteriorgrams -- Search space reduction -- Phonetic segmentation -- Bag-of-Acoustic-Word model; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.03.004
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Query-by-Example approach of spoken content retrieval has gained much attention because of its feasibility in the absence of speech recognition and its applicability in a multilingual matching scenario. This approach to retrieve spoken content is referred to as Query-by-Example Spoken Term Detection (QbE-STD). The state-of-the-art QbE-STD system performs matching between the frame sequence of query and test utterance via Dynamic Time Warping (DTW) algorithm. In realistic scenarios, there is a need to retrieve the query which does not appear exactly in the spoken document. However, the appeared instance of query might have the different suffix, prefix or word order. The DTW algorithm monotonically aligns the two sequences and hence, it is not suitable to perform partial matching between the frame sequence of query and test utterance. In this paper, we propose novel partial matching approach between spoken query and utterance using modified DTW algorithm where multiple warping paths are constructed for each query and test utterance pair. Next, we address the research issue associated with search complexity of DTW and suggest two approaches, namely, feature reduction approach and Bag-of-Acoustic-Words (BoAW) model. In feature reduction approach, the number of feature vectors is reduced by averaging across the consecutive frames within phonetic boundaries. Thus, a lesser number of feature vectors require fewer number of comparisons and hence, DTW speeds up the search computation. The search computation time gets reduced by 46 – 49 % with a slight degradation in performance as compared to no feature reduction case. In BoAW model, we construct term frequency-inverse document frequency ( t f − i d f ) vectors at segment-level to retrieve audio documents. The proposed segment-level BoAW model is used to match test utterance with a query using ( t f − i d f ) vectors and the scores obtained are used to rank the test utterance. The BoAW model gave more than 80 % recall value on 70 % top retrieval. To re-score the detection, we further employ DTW search or modified DTW search to retrieve the spoken query from the selected utterances using BoAW model. QbE-STD experiments are conducted on different international benchmarks, namely, MediaEval spoken web search SWS 2013 and MediaEval query-by-example search on speech QUESST 2014.