An Iterative Deep Learning Framework for Unsupervised Discovery of Speech Features and Linguistic Units with Applications on Spoken Term Detection
Table 3: Overall spoken term detection performance in mean average precision.
['method', 'index', 'index', 'MAP(%) Eng', 'MAP(%) Xit']
The benefit of the iterative framework of Mutual Reinforcement (MR) can be observed by comparing rows (a) to (b), (b) to (c) and (b) to (d) (MR-0 vs MR-1, MR-1 vs MR-2). The benefit of the iterative framework of the MAT-DNN can be observed by comparing row (a) to (d) and (b) to (e) (TOK-1st vs TOK-2nd). We then averaged all token DTW distances in (a) to (e) in row(h), and obtained better results, showing that the information obtained in each collection is complimentary to each other as well. We then compared these results with two cases of DTW performed on frame-level features: 39-dim MFCC in row (f) and bottleneck features (BNF-1st, MR-1) in row (g). By comparing rows (g) to (f), we observe that the features obtained by MAT-DNN performed significantly better than the MFCC from which they were derived. We further fused the information from both the feature based DTW and token based DTW by averaging all scores in rows (a) to (g) in row (i), producing even better results indicating frame-level and token-level information are complementary.