Manifold Integration: Data Integration on Multiple Manifolds

In data analysis, data points are usually analyzed based on their relations to other points (e.g., distance or inner product). This kind of relation can be analyzed on the manifold of the data set. Manifold learning is an approach to understand such relations. Various manifold learning methods have been developed and their effectiveness has been demonstrated in many real-world problems in pattern recognition and signal processing. However, most existing manifold learning algorithms only consider one manifold based on one dissimilarity matrix. In practice, multiple measurements may be available, and could be utilized. In pattern recognition systems, data integration has been an important consideration for improved accuracy given multiple measurements. Some data integration algorithms have been proposed to address this issue. These integration algorithms mostly use statistical information from the data set such as uncertainty of each data source, but they do not use the structural information (i.e., the geometric relations between data points). Such a structure is naturally described by a manifold. Even though manifold learning and data integration have been successfully used for data analysis, they have not been considered in a single integrated framework. When we have multiple measurements generated from the same data set and mapped onto different manifolds, those measurements can be integrated using the structural information on these multiple manifolds. Furthermore, we can better understand the structure of the data set by combining multiple measurements in each manifold using data integration techniques. In this dissertation, I present a new concept, manifold integration, a data integration method using the structure of data expressed in multiple manifolds. In order to achieve manifold integration, I formulated the manifold integration concept, and derived three manifold integration algorithms. Experimental results showed the algorithms' effectiveness in classification and dimension reduction. Moreover, for manifold integration, I showed that there are good theoretical and neuroscientific applications. I expect the manifold integration approach to serve as an effective framework for analyzing multimodal data sets on multiple manifolds. Also, I expect that my research on manifold integration will catalyze both manifold learning and data integration research

Speech analysis using very low-dimensional bottleneck features and phone-class dependent neural networks

The first part of this thesis focuses on very low-dimensional bottleneck features (BNFs), extracted from deep neural networks (DNNs) for speech analysis and recognition. Very low-dimensional BNFs are analysed in terms of their capability of representing speech and their suitability for modelling speech dynamics. Nine-dimensional BNFs obtained from a phone discrimination DNN are shown to give comparable phone recognition accuracy to 39-dimensional MFCCs, and an average of 34% higher phone recognition accuracy than formant-based features of the same dimensions. They also preserve the trajectory continuity well and thus hold promise for modelling speech dynamics. Visualisations and interpretations of the BNFs are presented, with phonetically motivated studies of the strategies that DNNs employ to create these features. The relationships between BNF representations resulting from different initialisations of DNNs are explored. The second part of this thesis considers BNFs from the perspective of feature extraction. It is motivated by the observation that different types of speech sounds lend themselves to different acoustic analysis, and that the mapping from spectra-in-context to phone posterior probabilities implemented by the DNN is a continuous approximation to a discontinuous function. This suggests that it may be advantageous to replace the single DNN with a set of phone class dependent DNNs. In this case, the appropriate mathematical structure is a manifold. It is shown that this approach leads to significant improvements in frame level phone classification accuracy

Cortical encoding and decoding models of speech production

To speak is to dynamically orchestrate the movements of the articulators (jaw, tongue, lips, and larynx), which in turn generate speech sounds. It is an amazing mental and motor feat that is controlled by the brain and is fundamental for communication. Technology that could translate brain signals into speech would be transformative for people who are unable to communicate as a result of neurological impairments. This work first investigates how articulator movements that underlie natural speech production are represented in the brain. Building upon this, this work also presents a neural decoder that can synthesize audible speech from brain signals. Data to support these results were from direct cortical recordings of the human sensorimotor cortex while participants spoke natural sentences. Neural activity at individual electrodes encoded a diversity of articulatory kinematic trajectories (AKTs), each revealing coordinated articulator movements towards specific vocal tract shapes. The neural decoder was designed to leverage the kinematic trajectories encoded in the sensorimotor cortex which enhanced performance even with limited data. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication

Neighborhood analysis methods in acoustic modeling for automatic speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 121-134).This thesis investigates the problem of using nearest-neighbor based non-parametric methods for performing multi-class class-conditional probability estimation. The methods developed are applied to the problem of acoustic modeling for speech recognition. Neighborhood components analysis (NCA) (Goldberger et al. [2005]) serves as the departure point for this study. NCA is a non-parametric method that can be seen as providing two things: (1) low-dimensional linear projections of the feature space that allow nearest-neighbor algorithms to perform well, and (2) nearest-neighbor based class-conditional probability estimates. First, NCA is used to perform dimensionality reduction on acoustic vectors, a commonly addressed problem in speech recognition. NCA is shown to perform competitively with another commonly employed dimensionality reduction technique in speech known as heteroscedastic linear discriminant analysis (HLDA) (Kumar [1997]). Second, a nearest neighbor-based model related to NCA is created to provide a class-conditional estimate that is sensitive to the possible underlying relationship between the acoustic-phonetic labels. An embedding of the labels is learned that can be used to estimate the similarity or confusability between labels. This embedding is related to the concept of error-correcting output codes (ECOC) and therefore the proposed model is referred to as NCA-ECOC. The estimates provided by this method along with nearest neighbor information is shown to provide improvements in speech recognition performance (2.5% relative reduction in word error rate). Third, a model for calculating class-conditional probability estimates is proposed that generalizes GMM, NCA, and kernel density approaches. This model, called locally-adaptive neighborhood components analysis, LA-NCA, learns different low-dimensional projections for different parts of the space. The models exploits the fact that in different parts of the space different directions may be important for discrimination between the classes. This model is computationally intensive and prone to over-fitting, so methods for sub-selecting neighbors used for providing the classconditional estimates are explored. The estimates provided by LA-NCA are shown to give significant gains in speech recognition performance (7-8% relative reduction in word error rate) as well as phonetic classification.by Natasha Singh-Miller.Ph.D

Computer analysis of children's non-native English speech for language learning and assessment

Children's ASR appears to be more challenging than adults' and it's even more difficult when it comes to non-native children's speech. This research investigates different techniques to compensate for the effects of non-native and children on the performance of ASR systems. The study mainly utilises hybrid DNN-HMM systems with conventional DNNs, LSTMs and more advanced TDNN models. This work uses the CALL-ST corpus and TLT-school corpus to study children's non-native English speech. Initially, data augmentation was explored on the CALL-ST corpus to address the lack of data problem using the AMI corpus and PF-STAR German corpus. Feature selection, acoustic model adaptation and selection were also investigated on CALL-ST. More aspects of the ASR system, including pronunciation modelling, acoustic modelling, language modelling and system fusion, were explored on the TLT-school corpus as this corpus has a bigger amount of data. Then, the relationships between the CALL-ST and TLT-school corpora were studied and utilised to improve ASR performance. The other part of the present work is text processing for non-native children's English speech. We focused on providing accept/reject feedback to learners based on the text generated by the ASR system from learners' spoken responses. A rule-based and a machine learning-based system were proposed for making the judgement, several aspects of the systems were evaluated. The influence of the ASR system on the text processing system was explored

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis