Learning representations for speech recognition using artificial neural networks

Learning representations is a central challenge in machine learning. For speech recognition, we are interested in learning robust representations that are stable across different acoustic environments, recording equipment and irrelevant inter– and intra– speaker variabilities. This thesis is concerned with representation learning for acoustic model adaptation to speakers and environments, construction of acoustic models in low-resource settings, and learning representations from multiple acoustic channels. The investigations are primarily focused on the hybrid approach to acoustic modelling based on hidden Markov models and artificial neural networks (ANN). The first contribution concerns acoustic model adaptation. This comprises two new adaptation transforms operating in ANN parameters space. Both operate at the level of activation functions and treat a trained ANN acoustic model as a canonical set of fixed-basis functions, from which one can later derive variants tailored to the specific distribution present in adaptation data. The first technique, termed Learning Hidden Unit Contributions (LHUC), depends on learning distribution-dependent linear combination coefficients for hidden units. This technique is then extended to altering groups of hidden units with parametric and differentiable pooling operators. We found the proposed adaptation techniques pose many desirable properties: they are relatively low-dimensional, do not overfit and can work in both a supervised and an unsupervised manner. For LHUC we also present extensions to speaker adaptive training and environment factorisation. On average, depending on the characteristics of the test set, 5-25% relative word error rate (WERR) reductions are obtained in an unsupervised two-pass adaptation setting. The second contribution concerns building acoustic models in low-resource data scenarios. In particular, we are concerned with insufficient amounts of transcribed acoustic material for estimating acoustic models in the target language – thus assuming resources like lexicons or texts to estimate language models are available. First we proposed an ANN with a structured output layer which models both context–dependent and context–independent speech units, with the context-independent predictions used at runtime to aid the prediction of context-dependent states. We also propose to perform multi-task adaptation with a structured output layer. We obtain consistent WERR reductions up to 6.4% in low-resource speaker-independent acoustic modelling. Adapting those models in a multi-task manner with LHUC decreases WERRs by an additional 13.6%, compared to 12.7% for non multi-task LHUC. We then demonstrate that one can build better acoustic models with unsupervised multi– and cross– lingual initialisation and find that pre-training is a largely language-independent. Up to 14.4% WERR reductions are observed, depending on the amount of the available transcribed acoustic data in the target language. The third contribution concerns building acoustic models from multi-channel acoustic data. For this purpose we investigate various ways of integrating and learning multi-channel representations. In particular, we investigate channel concatenation and the applicability of convolutional layers for this purpose. We propose a multi-channel convolutional layer with cross-channel pooling, which can be seen as a data-driven non-parametric auditory attention mechanism. We find that for unconstrained microphone arrays, our approach is able to match the performance of the comparable models trained on beamform-enhanced signals

Time-frequency shift-tolerance and counterpropagation network with applications to phoneme recognition

Human speech signals are inherently multi-component non-stationary signals. Recognition schemes for classification of non-stationary signals generally require some kind of temporal alignment to be performed. Examples of techniques used for temporal alignment include hidden Markov models and dynamic time warping. Attempts to incorporate temporal alignment into artificial neural networks have resulted in the construction of time-delay neural networks. The nonstationary nature of speech requires a signal representation that is dependent on time. Time-frequency signal analysis is an extension of conventional time-domain and frequency-domain analysis methods. Researchers have reported on the effectiveness of time-frequency representations to reveal the time-varying nature of speech. In this thesis, a recognition scheme is developed for temporal-spectral alignment of nonstationary signals by performing preprocessing on the time-frequency distributions of the speech phonemes. The resulting representation is independent of any amount of time-frequency shift and is time-frequency shift-tolerant (TFST). The proposed scheme does not require time alignment of the signals and has the additional merit of providing spectral alignment, which may have importance in recognition of speech from different speakers. A modification to the counterpropagation network is proposed that is suitable for phoneme recognition. The modified network maintains the simplicity and competitive mechanism of the counterpropagation network and has additional benefits of fast learning and good modelling accuracy. The temporal-spectral alignment recognition scheme and modified counterpropagation network are applied to the recognition task of speech phonemes. Simulations show that the proposed scheme has potential in the classification of speech phonemes which have not been aligned in time. To facilitate the research, an environment to perform time-frequency signal analysis and recognition using artificial neural networks was developed. The environment provides tools for time-frequency signal analysis and simulations of of the counterpropagation network

WekaDeeplearning4j: A deep learning package for weka based on Deeplearning4j

Deep learning is a branch of machine learning that generates multi-layered representations of data, commonly using artificial neural networks, and has improved the state-of-the-art in various machine learning tasks (e.g., image classification, object detection, speech recognition, and document classification). However, most popular deep learning frameworks such as TensorFlow and PyTorch require users to write code to apply deep learning. We present WekaDeeplearning4j, a Weka package that makes deep learning accessible through a graphical user interface (GUI). The package uses Deeplearning4j as its backend, provides GPU support, and enables GUI-based training of deep neural networks such as convolutional and recurrent neural networks. It also provides pre-processing functionality for image and text data

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar