  Traditional automatic speech recognition (ASR) systems often use an acoustic
model (AM) built on handcrafted acoustic features, such as log Mel-filter bank
(FBANK) values. Recent studies found that AMs with convolutional neural
networks (CNNs) can directly use the raw waveform signal as input. Given
sufficient training data, these AMs can yield a competitive word error rate
(WER) to those built on FBANK features. This paper proposes a novel multi-span
structure for acoustic modelling based on the raw waveform with multiple
streams of CNN input layers, each processing a different span of the raw
waveform signal. Evaluation on both the single channel CHiME4 and AMI data sets
show that multi-span AMs give a lower WER than FBANK AMs by an average of about
5% (relative). Analysis of the trained multi-span model reveals that the CNNs
can learn filters that are rather different to the log Mel filters.
Furthermore, the paper shows that a widely used single span raw waveform AM can
be improved by using a smaller CNN kernel size and increased stride to yield
improved WERs.
