  Raw waveform acoustic modelling has recently gained interest due to neural
networks' ability to learn feature extraction, and the potential for finding
better representations for a given scenario than hand-crafted features. SincNet
has been proposed to reduce the number of parameters required in raw-waveform
modelling, by restricting the filter functions, rather than having to learn
every tap of each filter. We study the adaptation of the SincNet filter
parameters from adults' to children's speech, and show that the
parameterisation of the SincNet layer is well suited for adaptation in
practice: we can efficiently adapt with a very small number of parameters,
producing error rates comparable to techniques using orders of magnitude more
parameters.
