Neural Network based End-to-End Query by Example Spoken Term Detection

--This paper focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. State-of-the-art approaches primarily rely on dynamic time warping (DTW) based template matching techniques using phone posterior or bottleneck features extracted from a deep neural network (DNN). We use both monolingual and multilingual bottleneck features, and show that multilingual features perform increasingly better with more training languages. Previously, it has been shown that the DTW based matching can be replaced with a CNN based matching while using posterior features. Here, we show that the CNN based matching outperforms DTW based matching using bottleneck features as well. In this case, the feature extraction and pattern matching stages of our QbE-STD system are optimized independently of each other . We propose to integrate these two stages in a fully neural network based end-to-end learning framework to enable joint optimization of those two stages simultaneously. The proposed approaches are evaluated on two challenging multilingual datasets: Spoken Web Search 2013 and Query by Example Search on Speech T ask 2014, demonstrating in each case significant improvements. Query-by-example spoken term detection (QbE-STD) is defined as the task of detecting all files from an audio archive which contain a spoken query provided by a user (see Figure 1). It enables users to search through multilingual audio archives using their own speech. The primary difference from keyword spotting is that QbE-STD relies on spoken queries instead of textual queries making it a language independent task. In general, the queries and test utterances are generated by different speakers in different languages with varying acoustic conditions and without constraints on vocabulary, pronunciation lexicon, accents etc. Thus, the search is performed relying only on acoustic data of the query and test utterances with no language specific resources, as a zero-resource task. It is essentially a pattern matching problem in the context of speech data where the targeted pattern is the information represented using speech signal and given to the system as a spoken query.