Two‐stage spoken term detection system for under‐resourced languages. Issue 9 (6th October 2020)

• Record Type: Journal Article
• Authors: G, Deekshitha; Mary, Leena
• Is Part Of: IET signal processing. Volume 14:Issue 9(2020)
• Page Start: 602
• Page End: 613
• Publication Date: 2020-10-06
• Subjects: speech recognition -- probability -- feature extraction -- speech processing -- natural language processing -- image matching -- query processing; stage spoken term detection system -- spoken queries -- given speech database -- automatic speech recognition based sequence matching -- ASR‐free -- STD method -- labelled corpora -- template matching methods work -- search database -- time complexity -- template matching techniques -- two‐stage STD -- phoneme label sequence matching -- feature sequence template matching -- available annotated corpora -- sequence matching technique -- erroneous label sequences -- feature level template matching -- probable query locations -- template matching approach -- longer query words -- labelled story database -- STD task -- STD system -- query length -- time 3.5 hour; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2019.0131
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : Spoken Term Detection (STD) is the process of locating the occurrences of spoken queries in a given speech database. Generally, two methods are adopted for STD: an ASR based sequence matching and ASR‐free, feature‐based template matching. If a well‐performing ASR is available, the former STD method is accurate. However, to build an ASR with consistent performance, several hours of labelled corpora is required. Template matching methods work well for small or chopped utterances. However, in practice, the volume of the search database can be huge, containing sentences of varying lengths. Hence time complexity of template matching techniques will be high, which makes them impractical for realistic search applications. In this work, a two‐stage STD system is proposed, which combines the ASR‐based phoneme sequence matching in the first stage and feature sequence template matching of selected locations in the second stage. The time complexity of the second stage is reduced by performing DTW‐based template matching only at probable query locations identified by the first stage. 'Split and match' approach helps to reduce the false‐positives in case of longer query words. Effectiveness of the proposed method is demonstrated using English and Malayalam datasets.