  Recurrent neural networks (RNN) are at the core of modern automatic speech
recognition (ASR) systems. In particular, long-short term memory (LSTM)
recurrent neural networks have achieved state-of-the-art results in many speech
recognition tasks, due to their efficient representation of long and short term
dependencies in sequences of inter-dependent features. Nonetheless, internal
dependencies within the element composing multidimensional features are weakly
considered by traditional real-valued representations. We propose a novel
quaternion long-short term memory (QLSTM) recurrent neural network that takes
into account both the external relations between the features composing a
sequence, and these internal latent structural dependencies with the quaternion
algebra. QLSTMs are compared to LSTMs during a memory copy-task and a realistic
application of speech recognition on the Wall Street Journal (WSJ) dataset.
QLSTM reaches better performances during the two experiments with up to $2.8$
times less learning parameters, leading to a more expressive representation of
the information.
