Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach

Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or power demand, recent interest for neural networks and bio-inspired systems has motivated the implementation of new techniques. Among them, a combination of spiking neural networks and neuromorphic auditory sensors offer an alternative to carry out the human-like speech processing task. In this approach, a spiking convolutional neural network model was implemented, in which the weights of connections were calculated by training a convolutional neural network with specific activation functions, using firing rate-based static images with the spiking information obtained from a neuromorphic cochlea. The system was trained and tested with a large dataset that contains ”left” and ”right” speech commands, achieving 89.90% accuracy. A novel spiking neural network model has been proposed to adapt the network that has been trained with static images to a non-static processing approach, making it possible to classify audio signals and time series in real time.Ministerio de Economía y Competitividad TEC2016-77785-

Frame-by-frame language identification in short utterances using deep neural networks

This is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, VOL 64, (2015) DOI 10.1016/j.neunet.2014.08.006This work addresses the use of deep neural networks (DNNs) in automatic language identification (LID) focused on short test utterances. Motivated by their recent success in acoustic modelling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from the short-term acoustic features. We show how DNNs are particularly suitable to perform LID in real-time applications, due to their capacity to emit a language identification posterior at each new frame of the test utterance. We then analyse different aspects of the system, such as the amount of required training data, the number of hidden layers, the relevance of contextual information and the effect of the test utterance duration. Finally, we propose several methods to combine frame-by-frame posteriors. Experiments are conducted on two different datasets: the public NIST Language Recognition Evaluation 2009 (3 s task) and a much larger corpus (of 5 million utterances) known as Google 5M LID, obtained from different Google Services. Reported results show relative improvements of DNNs versus the i-vector system of 40% in LRE09 3 second task and 76% in Google 5M LID

Augmented reality applied to language translation

Being a tourist in a foreign country is an adventure full of memories and experiences, but it can be truly challenging when it comes to communication. Finding yourself in an unknown place, where all the road signs and guidelines have such different characters, may end up in a dead end or with some unexpected results. Then, what if we could use a smartphone to read that restaurant menu? Or even find the right department in a mall? The applications are so many and the market is ready to invest and give opportunities to creative and economic ideas. The dissertation intends to explore the field of Augmented Reality, while helping the user to enrich his view with information. Giving the ability to look around, detect the text in the surroundings and read its translation in our own dialect, is a great step to overcome language issues. Moreover, using smartphones at anyone’s reach, or wearing smartglasses that are even less intrusive, gives a chance to engage a complex matter in a daily routine. This technology requires flexible, accurate and fast Optical Character Recognition and Translation systems, in an Internet of Things scenery. Quality and precision is a must, yet to be further developed and improved. Entering in a realtime digital data environment, will support great causes and aid the progress and evolution of many intervention areas

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

Real-Time Subtitle Generator for Sinhala Speech

In today’s digital era, the significance of speech recognition technology cannot be overstated as it plays a pivotal role in enabling human-computer interaction and supporting various applications. This paper focuses on the development of a real-time subtitle generator for Sinhala speech using speech recognition techniques. The CMUSphinx toolkit, an open-source toolkit based on the Hidden Markov Model (HMM), is employed for the implementation of the application. Mel-frequency cepstral coefficients (MFCC) are utilized for feature extraction from the given ’wav’ format recordings. The paper places significant emphasis on the importance of a real-time subtitle generator for Sinhala speech and explores the existing literature in the field. It outlines the objectives of the research and discusses the achieved outcomes. By fine-tuning hyperparameters to enhance the recognition accuracy of the system, impressive results of 88.28% training accuracy and 11.72% Word Error Rate (WER) are attained. Thesignificance of this research is underscored by its methodological advancements, robust performance metrics, and the potential impact on facilitating seamless interactions and applications in the Sinhala speech domain. Keywords: Speech recognition, Real-time, Subtitle, CMUSphinx, Open source, Hidden Markov Model, Mel-frequency cepstral coefficients, ’wav’, Accuracy, Word Error Rat

Voice Recognition Systems for The Disabled Electorate: Critical Review on Architectures and Authentication Strategies

An inevitable factor that makes the concept of electronic voting irresistible is the fact that it offers the possibility of exceeding the manual voting process in terms of convenience, widespread participation, and consideration for People Living with Disabilities. The underlying voting technology and ballot design can determine the credibility of election results, influence how voters felt about their ability to exercise their right to vote, and their willingness to accept the legitimacy of electoral results. However, the adoption of e-voting systems has unveiled a new set of problems such as security threats, trust, and reliability of voting systems and the electoral process itself. This paper presents a critical literature review on concepts, architectures, and existing authentication strategies in voice recognition systems for the e-voting system for the disabled electorate. Consequently, in this paper, an intelligent yet secure scheme for electronic voting systems specifically for people living with disabilities is presented