200 research outputs found
Real to H-space Encoder for Speech Recognition
International audienceDeep neural networks (DNNs) and more precisely recurrent neural networks (RNNs) are at the core of modern automatic speech recognition systems, due to their efficiency to process input sequences. Recently, it has been shown that different input representations, based on multidimensional algebras, such as complex and quaternion numbers, are able to bring to neural networks a more natural, compressive and powerful representation of the input signal by outperforming common real-valued NNs. Indeed, quaternion-valued neural networks (QNNs) better learn both internal dependencies, such as the relation between the Mel-filter-bank value of a specific time frame and its time derivatives, and global dependencies, describing the relations that exist between time frames. Nonetheless, QNNs are limited to quaternion-valued input signals, and it is difficult to benefit from this powerful representation with real-valued input data. This paper proposes to tackle this weakness by introducing a real-to-quaternion encoder that allows QNNs to process any one dimensional input features, such as traditional Mel-filter-banks for automatic speech recognition
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition
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.Comment: Accepted at INTERSPEECH 201
A Quaternion Gated Recurrent Unit Neural Network for Sensor Fusion
Recurrent Neural Networks (RNNs) are known for their ability to learn relationships within temporal sequences. Gated Recurrent Unit (GRU) networks have found use in challenging time-dependent applications such as Natural Language Processing (NLP), financial analysis and sensor fusion due to their capability to cope with the vanishing gradient problem. GRUs are also known to be more computationally efficient than their variant, the Long Short-Term Memory neural network (LSTM), due to their less complex structure and as such, are more suitable for applications requiring more efficient management of computational resources. Many of such applications require a stronger mapping of their features to further enhance the prediction accuracy. A novel Quaternion Gated Recurrent Unit (QGRU) is proposed in this paper, which leverages the internal and external dependencies within the quaternion algebra to map correlations within and across multidimensional features. The QGRU can be used to efficiently capture the inter- and intra-dependencies within multidimensional features unlike the GRU, which only captures the dependencies within the sequence. Furthermore, the performance of the proposed method is evaluated on a sensor fusion problem involving navigation in Global Navigation Satellite System (GNSS) deprived environments as well as a human activity recognition problem. The results obtained show that the QGRU produces competitive results with almost 3.7 times fewer parameters compared to the GRU
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
Many state-of-the-art neural models for NLP are heavily parameterized and
thus memory inefficient. This paper proposes a series of lightweight and memory
efficient neural architectures for a potpourri of natural language processing
(NLP) tasks. To this end, our models exploit computation using Quaternion
algebra and hypercomplex spaces, enabling not only expressive inter-component
interactions but also significantly () reduced parameter size due to
lesser degrees of freedom in the Hamilton product. We propose Quaternion
variants of models, giving rise to new architectures such as the Quaternion
attention Model and Quaternion Transformer. Extensive experiments on a battery
of NLP tasks demonstrates the utility of proposed Quaternion-inspired models,
enabling up to reduction in parameter size without significant loss in
performance.Comment: ACL 201
Analyzing Input and Output Representations for Speech-Driven Gesture Generation
This paper presents a novel framework for automatic speech-driven gesture
generation, applicable to human-agent interaction including both virtual agents
and robots. Specifically, we extend recent deep-learning-based, data-driven
methods for speech-driven gesture generation by incorporating representation
learning. Our model takes speech as input and produces gestures as output, in
the form of a sequence of 3D coordinates. Our approach consists of two steps.
First, we learn a lower-dimensional representation of human motion using a
denoising autoencoder neural network, consisting of a motion encoder MotionE
and a motion decoder MotionD. The learned representation preserves the most
important aspects of the human pose variation while removing less relevant
variation. Second, we train a novel encoder network SpeechE to map from speech
to a corresponding motion representation with reduced dimensionality. At test
time, the speech encoder and the motion decoder networks are combined: SpeechE
predicts motion representations based on a given speech signal and MotionD then
decodes these representations to produce motion sequences. We evaluate
different representation sizes in order to find the most effective
dimensionality for the representation. We also evaluate the effects of using
different speech features as input to the model. We find that mel-frequency
cepstral coefficients (MFCCs), alone or combined with prosodic features,
perform the best. The results of a subsequent user study confirm the benefits
of the representation learning.Comment: Accepted at IVA '19. Shorter version published at AAMAS '19. The code
is available at
https://github.com/GestureGeneration/Speech_driven_gesture_generation_with_autoencode
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