Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition

By Titouan Parcollet, Y. Zhang, Mohamed Morchid, C. Trabelsi, Georges Linarès, R. Mori, Yoshua Bengio. 2018

116 citations recorded by the source corpus.

Recently, the connectionist temporal classification (CTC) model coupled with recurrent (RNN) or convolutional neural networks (CNN), made it easier to train speech recognition systems in an end-to-end fashion. However in real-valued models, time frame components such as mel-filter-bank energies and the cepstral coefficients obtained from them, together with their first and second order derivatives, are processed as individual elements, while a natural alternative is to process such components as composed entities. We propose to group such elements in the form of quaternions and to process these quaternions using the established quaternion algebra. Quaternion numbers and quaternion neural networks have shown their efficiency to process multidimensional inputs as entities, to encode internal dependencies, and to solve many tasks with less learning parameters than real-valued models. This paper proposes to integrate multiple feature views in quaternion-valued convolutional neural network (QCNN), to be used for sequence-to-sequence mapping with the CTC model. Promising results are reported using simple QCNNs in phoneme recognition experiments with the TIMIT corpus. More precisely, QCNNs obtain a lower phoneme error rate (PER) with less learning parameters than a competing model based on real-valued CNNs.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Compacter: Efficient Low-Rank Hypercomplex Adapter Layers (2021, background): “Hypercomplex representations Deep learning advances in the hypercomplex domain are in a nascent stage, and most work is fairly recent [40, 41, 42, 43, 44].”
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (2019, methodology): “While Quaternion connectionist architectures have been considered in various deep learning application areas such as speech recognition (Parcollet et al., 2018b), kinematics/human motion (Pavllo et al., 2018) and computer vision (Gaudet and Maida, 2017), our work is the ﬁrst hypercomplex inductive…”
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n Parameters (2021, methodology): “Though parameter reusing by component-wise partitioning in Quaternion space has demonstrated success (Parcollet et al., 2018b; Zhu et al., 2018; Parcollet et al., 2019; Tay et al., 2019), one key problem is that hypercomplex space only exists at very few predeﬁned dimensionalities, such as 4D…”
A Survey of Sound Source Localization with Deep Learning Methods (2021, methodology): “Based on the FOA spectrograms, they proposed considering them as quaternion-based input features, which proved to be a suitable representation in previous works [Parcollet et al., 2018].”
Quaternion Recurrent Neural Networks (2018, background): “In particular, a deep quaternion network (Parcollet et al., 2016; 2017a;b), a deep quaternion convolutional network (Gaudet & Maida, 2018; Parcollet et al., 2018), or a deep complex convolutional network (Trabelsi et al., 2017) have been employed for challenging tasks such as images and language…”