An improved uncertainty decoding scheme with weighted samples for DNN-HMM hybrid systems
Table 2: WER scores for the REVERB challenge evaluation test set achieved by the DNN-HMM hybrid system without (“No UD”) and with uncertainty decoding based on random sampling (L=30 samples) using arithmetic (“UD arithm”) and weighted (“UD weight”) DNN-output averaging.
['[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']
(manually optimized to a value of 0.1) to compensate for the inaccuracies of the variance estimation. First, the impact of the number of L samples on the recognition accuracy of the DNN-HMM hybrid system is evaluated with focus on the practically relevant case of real-world recordings. As shown in Fig. Further, replacing arithmetic (“UD arithm” in Fig. (“UD weight” in Fig. It should be mentioned that no significant further improvement was observed by increasing the number of samples above L=60. It is worth highlighting that uncertainty decoding based on random sampling modifies the posterior-likelihood calculation and leaves the remaining parts of the decoding procedure (e.g., grammar and language model) untouched. As a consequence, the average decoding time in our implementation is only increased by 76% for L=30 samples (Intel i7 with 3.4 GHz, GPU: NVIDIA GeForce GTX 650 Ti) over regular decoding (no uncertainty decoding).