Multi-head Monotonic Chunkwise Attention for Online Speech Recognition
Table 3: Experimental results (CER) on our in-car corpora InCar2000 and InCar18000.
['model', 'InCar2000 Dev', 'InCar2000 Test', 'InCar18000 Dev', 'InCar18000 Test']
To further verify the effectiveness of the MTH-MoChA model and other optimization training strategies for online attention model, we conduct experiments on another two large data sets, InCar2000 and InCar18000. TDNN-OPGRU is a hybrid model trained with SpecAugment and LFMMI. From the result on InCar2000, we can find that MoChA performs much worse than the vanilla LAS model. The reason for this may be that most recordings of the in-car corpus are speech enquiries and the average number of characters inside a recording is much less, meaning that the average duration of utterance segments is shorter. Compared with LAS, MoChA cannot use the whole audio segment to calculate the attention context. Therefore, the available information for attention context calculation is much less, especially for short recordings. For InCar2000 data set, MTH-MoChA obtains 10.42% and 15.30% CER the development and test sets respectively. It improves MoChA by 21.62% relatively on the test data. When combining with the other optimization training strategies, we can get another 9.09% relative reduction in CER. We did not train the LAS and MoChA models on the InCar18000 data set since it takes a long time.