  Neural network architectures are at the core of powerful automatic speech
recognition systems (ASR). However, while recent researches focus on novel
model architectures, the acoustic input features remain almost unchanged.
Traditional ASR systems rely on multidimensional acoustic features such as the
Mel filter bank energies alongside with the first, and second order derivatives
to characterize time-frames that compose the signal sequence. Considering that
these components describe three different views of the same element, neural
networks have to learn both the internal relations that exist within these
features, and external or global dependencies that exist between the
time-frames. Quaternion-valued neural networks (QNN), recently received an
important interest from researchers to process and learn such relations in
multidimensional spaces. Indeed, quaternion numbers and QNNs have shown their
efficiency to process multidimensional inputs as entities, to encode internal
dependencies, and to solve many tasks with up to four times less learning
parameters than real-valued models. We propose to investigate modern
quaternion-valued models such as convolutional and recurrent quaternion neural
networks in the context of speech recognition with the TIMIT dataset. The
experiments show that QNNs always outperform real-valued equivalent models with
way less free parameters, leading to a more efficient, compact, and expressive
representation of the relevant information.
