Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
Table 2: LER/WER of the best sets of hyper-parameters for each feature types.
['[EMPTY]', 'MFCC LER', 'MFCC WER', 'PS LER', 'PS WER', 'Raw LER', 'Raw WER']
every 20 ms. We found that one could squeeze out about 1% in performance by refining the precision of the output. This is efficiently achieved by shifting the input sequence, and feeding it to the network several times. Both power spectrum and raw features are performing slightly worse than MFCCs. the gap would vanish.