A Comparison of Normalization Techniques Applied to Latent Space Representations for Speech Analytics

International audienceIn the context of noisy environments, Automatic Speech Recognition (ASR) systems usually produce poor transcription quality which also negatively impact performance of speech analyt-ics. Various methods have then been proposed to compensate the bad effect of ASR errors, mainly by projecting transcribed words in an abstract space. In this paper, we seek to identify themes from dialogues of telephone conversation services using latent topic-spaces estimated from a latent Dirichlet allocation (LDA). As an outcome, a document can be represented with a vector containing probabilities to be associated to each topic estimated with LDA. This vector should nonetheless be normalized to condition document representations. We propose to compare the original LDA vector representation (without normalization) with two normalization approaches, the Eigen Factor Radial (EFR) and the Feature Warping (FW) methods, already successfully applied in speaker recognition field, but never compared and evaluated in the context of a speech analytic task. Results show the interest of these normalization techniques for theme identification tasks using automatic transcriptions The EFR normalization approach allows a gain of 3.67 and 3.06 points respectively in comparison to the absence of normalization and to the FW normalization technique

Deep learning for time series classification: a review

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Big Social Data and GIS: Visualize Predictive Crime

Social media is a desirable Big Data source used to examine the relationship between crime and social behavior. Observation of this connection is enriched within a geographic information system (GIS) rooted in environmental criminology theory, and produces several different results to substantiate such a claim. This paper presents the construction and implementation of a GIS artifact producing visualization and statistical outcomes to develop evidence that supports predictive crime analysis. An information system research prototype guides inquiry and uses crime as the dependent variable and a social media tweet corpus, operationalized via natural language processing, as the independent variable. This inescapable realization of social media as a predictive crime variable is prudent; researchers and practitioners will better appreciate its capability. Inclusive visual and statistical results are novel, represent state-of-the-art predictive analysis, increase the baseline R2 value by 7.26%, and support future predictive crime-based research when front-run with real-time social media