An improved uncertainty decoding scheme with weighted samples for DNN-HMM hybrid systems
Table 1: WER scores for the REVERB challenge evaluation test set using different feature types.
['[EMPTY]', 'SimData  [ITALIC] T60≈0.25\xa0s', 'SimData  [ITALIC] T60≈0.25\xa0s', 'SimData  [ITALIC] T60≈0.5\xa0s', 'SimData  [ITALIC] T60≈0.5\xa0s', 'SimData  [ITALIC] T60≈0.75\xa0s', 'SimData  [ITALIC] T60≈0.75\xa0s', 'SimData', 'RealData  [ITALIC] T60≈0.7\xa0s', 'RealData  [ITALIC] T60≈0.7\xa0s', 'RealData']
This is realized using “Karel’s implementation“ of the Kaldi Toolkit including generative pretraining (on restricted Bolzmann machines) and discriminative fine-tuning (using a mini-batch stochastic gradient descent approach) It should be emphasized that the front-end enhancement and feature extraction is identical during training and testing. The evaluation test set of the REVERB-Challenge task (∼5000 environmentally-distorted utterances) consists of … artificially corrupted data (“SimData”) using measured impulse responses (T60≈0.25 s, 0.5 s and 0.7 s), recorded noise sequences (added to the microphones signals with a signal-to-noise ratio of 20 dB) and source-microphone spacings of 0.5 m (“Near”) and 2 m (“Far”), multichannel recordings (“RealData”) in a reverberant (T60≈0.7 s) and noisy environment with source-microphone spacings of 1 m (“Near”) and 2.5 m (“Far”). As first experiment, we evaluate the performance of the DNN-HMM hybrid system without uncertainty decoding. Here we compare the recognition accuracy achieved by “Logmelspec+delta+diffuseness” features (see Fig.