Unsupervised query by example spoken term detection using features concatenated with Self-Organizing Map distances
In the task of the unsupervised query by example spoken term detection (QbE-STD), we concatenate the features extracted by a Self-Organizing Map (SOM) and features learned by an unsupervised GMM based model at the feature level to enhance the performance. More specifically, The SOM features are represented by the distances between the current feature vector and the weight vectors of SOM neurons learned in an unsupervised manner. After fetching these features, we apply sub-sequence Dynamic Time Warping (S-DTW) to detect the occurrences of keywords in the test data. We evaluate the performance of these features on the TIMIT English database. After concatenating the SOM features and the GMM based features together, we achieve an improvement of 7.77% and 7.74% on Mean Average Precision (MAP) and P@10 on average.
