Cumulative Adaptation for BLSTM Acoustic Models
Table 1: Comparison of features used for universal background model training. The i-vectors were extraced only from speech frames and have a dimension of 100. Word error rate is given on the full Hub5’00 dataset. The acoustic model is a BLSTM trained on gammatone filterbank features.
['[BOLD] i-vectors', '[BOLD] UBM Features', '[BOLD] WER [%]']
In this paper we use a i-vector adapted neural network acoustic model. On each frame we append a i-vector to the 40-dimensional Gammatone Filterbank (GT) Most prior work report that they use Mel-frequency cepstral coefficients (MFCCs) But we noticed that the i-vector adaptation was not sufficiently effective in adapting to test signals when using MFCCs to estimate the i-vectors. Therefore, we compared MFCC to GT features without further processing as well as with concatenated first and second order derivatives and with temporal context and linear discriminant analysis (LDA) for dimension reduction. Using Gammatone features with a context of 9 frames reduced to 60 dimensions with LDA gives us significantly better performance. We did not check if MFCCs would be better if the acoustic model would also be trained on them.