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Table 3: WER (%) achieved on the Librispeech development and test sets. The upper panel gives the results, with 100-hour of training data, reported at the Github repo of Kaldi and PyTorch-Kaldi. The middle and lower panel present the results achieved by ANN and SNN models in this work using 100-hour and 360-hour of training data, respectively. (AM: acoustic model,†: reported at Github repo)
['[EMPTY]', '[EMPTY]', '[BOLD] Train - 100h Dev', '[BOLD] Train - 100h Dev', '[BOLD] Train - 100h Test', '[BOLD] Train - 100h Test']
In the final set of ASR experiments, we train acoustic models using the official 100-hour and 360-hour training subsets of the Librispeech corpus to compare the recognition performance of ANN and SNN models in a standard LVCSR scenario. The absolute WER differences range from 0.1% to 0.6%. These marginal performance degradations of the SNN models is likely due to the reduced representation power of using discrete spike counts.