Deep Speech: Scaling up end-to-end speech recognition
Table 3: Published error rates (%WER) on Switchboard dataset splits. The columns labeled “SWB” and “CH” are respectively the easy and hard subsets of Hub5’00.
['Model', 'SWB', 'CH', 'Full']
The model from Vesely et al. (DNN-GMM sMBR) The performance of this model on the combined Hub5’00 test set is the best previously published result. When trained on the combined 2300 hours of data the Deep Speech system improves upon this baseline by 2.4% absolute WER and 13.0% relative. The model from Maas et al. (DNN-HMM FSH) We include this result to demonstrate that Deep Speech, when trained on a comparable amount of data is competitive with the best existing ASR systems.