Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech

The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One prominent clinical application is a computer-assisted speech training system which enables personalized speech therapy to patients impaired by communicative disorders in the patient's home environment. Such a system relies on the robust automatic speech recognition (ASR) technology to be able to provide accurate articulation feedback. With the long-term aim of developing off-the-shelf ASR systems that can be incorporated in clinical context without prior speaker information, we compare the ASR performance of speaker-independent bottleneck and articulatory features on dysarthric speech used in conjunction with dedicated neural network-based acoustic models that have been shown to be robust against spectrotemporal deviations. We report ASR performance of these systems on two dysarthric speech datasets of different characteristics to quantify the achieved performance gains. Despite the remaining performance gap between the dysarthric and normal speech, significant improvements have been reported on both datasets using speaker-independent ASR architectures.Comment: to appear in Computer Speech & Language - https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial text overlap with arXiv:1807.1094

Deep HMResNet Model for Human Activity-Aware Robotic Systems

Endowing the robotic systems with cognitive capabilities for recognizing daily activities of humans is an important challenge, which requires sophisticated and novel approaches. Most of the proposed approaches explore pattern recognition techniques which are generally based on hand-crafted features or learned features. In this paper, a novel Hierarchal Multichannel Deep Residual Network (HMResNet) model is proposed for robotic systems to recognize daily human activities in the ambient environments. The introduced model is comprised of multilevel fusion layers. The proposed Multichannel 1D Deep Residual Network model is, at the features level, combined with a Bottleneck MLP neural network to automatically extract robust features regardless of the hardware configuration and, at the decision level, is fully connected with an MLP neural network to recognize daily human activities. Empirical experiments on real-world datasets and an online demonstration are used for validating the proposed model. Results demonstrated that the proposed model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606

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

The information bottleneck method

We define the relevant information in a signal $x\in X$ as being the information that this signal provides about another signal y\in \Y. Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal $x$ requires more than just predicting $y$, it also requires specifying which features of \X play a role in the prediction. We formalize this problem as that of finding a short code for \X that preserves the maximum information about \Y. That is, we squeeze the information that \X provides about \Y through a `bottleneck' formed by a limited set of codewords \tX. This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure d(x,\x) emerges from the joint statistics of \X and \Y. This approach yields an exact set of self consistent equations for the coding rules X \to \tX and \tX \to \Y. Solutions to these equations can be found by a convergent re-estimation method that generalizes the Blahut-Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere