Sistema baseado em software livre para reconhecimento de fala em nuvem em português brasileiro com alta disponibilidade

Este trabalho visa propor uma solução contendo um sistema de reconhecimento de fala automático em nuvem. Dessa forma, não há necessidade de um reconhecedor sendo executado na própria máquina cliente, pois o mesmo estará disponível através da Internet. Além do reconhecimento automático de voz em nuvem, outra vertente deste trabalho é alta disponibilidade. A importância desse tópico se d´a porque o ambiente servidor onde se planeja executar o reconhecimento em nuvem não pode ficar indisponível ao usuário. Dos vários aspectos que requerem robustez, tal como a própria conexão de Internet, o escopo desse trabalho foi definido como os softwares livres que permitem a empresas aumentarem a disponibilidade de seus serviços. Dentre os resultados alcançados e para as condições simuladas, mostrou-se que o reconhecedor de voz em nuvem desenvolvido pelo grupo atingiu um desempenho próximo ao do Google.

Desarrollo de un sistema de reconocimiento facial

El objetivo principal alrededor del cual se desenvuelve este proyecto es el desarrollo de un sistema de reconocimiento facial. Entre sus objetivos específicos se encuentran: realizar una primera aproximación sobre las técnicas de reconocimiento facial existentes en la actualidad, elegir una aplicación donde pueda ser útil el reconocimiento facial, diseñar y desarrollar un programa en MATLAB que lleve a cabo la función de reconocimiento facial, y evaluar el funcionamiento del sistema desarrollado. Este documento se encuentra dividido en cuatro partes: INTRODUCCIÓN, MARCO TEÓRICO, IMPLEMENTACIÓN, y RESULTADOS, CONCLUSIONES Y LÍNEAS FUTURAS. En la primera parte, se hace una introducción relativa a la actualidad del reconocimiento facial y se comenta brevemente sobre las técnicas existentes para desarrollar un sistema biométrico de este tipo. En ella se justifican también aquellas técnicas que acabaron formando parte de la implementación. En la segunda parte, el marco teórico, se explica la estructura general que tiene un sistema de reconocimiento biométrico, así como sus modos de funcionamiento, y las tasas de error utilizadas para evaluar y comparar su rendimiento. Así mismo, se lleva a cabo una descripción más profunda sobre los conceptos y métodos utilizados para efectuar la detección y reconocimiento facial en la tercera parte del proyecto. La tercera parte abarca una descripción detallada de la solución propuesta. En ella se explica el diseño, características y aplicación de la implementación; que trata de un programa elaborado en MATLAB con interfaz gráfica, y que utiliza cuatro sistemas de reconocimiento facial, basados cada uno en diferentes técnicas: Análisis por componentes principales, análisis lineal discriminante, wavelets de Gabor, y emparejamiento de grafos elásticos. El programa ofrece además la capacidad de crear y editar una propia base de datos con etiquetas, dándole aplicación directa sobre el tema que se trata. Se proponen además una serie de características con el objetivo de ampliar y mejorar las funcionalidades del programa diseñado. Dentro de dichas características destaca la propuesta de un modo de verificación híbrido aplicable a cualquier rama de la biometría y un programa de evaluación capaz de medir, graficar, y comparar las configuraciones de cada uno de los sistemas de reconocimiento implementados. Otra característica destacable es la herramienta programada para la creación de grafos personalizados y generación de modelos, aplicable a reconocimiento de objetos en general. En la cuarta y última parte, se presentan al principio los resultados obtenidos. En ellos se contemplan y analizan las comparaciones entre las distintas configuraciones de los sistemas de reconocimiento implementados para diferentes bases de datos (una de ellas formada con imágenes con condiciones de adquisición no controladas). También se miden las tasas de error del modo de verificación híbrido propuesto. Finalmente, se extraen conclusiones, y se proponen líneas futuras de investigación. ABSTRACT The main goal of this project is to develop a facial recognition system. To meet this end, it was necessary to accomplish a series of specific objectives, which were: researching on the existing face recognition technics nowadays, choosing an application where face recognition might be useful, design and develop a face recognition system using MATLAB, and measure the performance of the implemented system. This document is divided into four parts: INTRODUCTION, THEORTICAL FRAMEWORK, IMPLEMENTATION, and RESULTS, CONCLUSSIONS AND FUTURE RESEARCH STUDIES. In the first part, an introduction is made in relation to facial recognition nowadays, and the techniques used to develop a biometric system of this kind. Furthermore, the techniques chosen to be part of the implementation are justified. In the second part, the general structure and the two basic modes of a biometric system are explained. The error rates used to evaluate and compare the performance of a biometric system are explained as well. Moreover, a description of the concepts and methods used to detect and recognize faces in the third part is made. The design, characteristics, and applications of the systems put into practice are explained in the third part. The implementation consists in developing a program with graphical user interface made in MATLAB. This program uses four face recognition systems, each of them based on a different technique: Principal Component Analysis (PCA), Fisher’s Linear Discriminant (FLD), Gabor wavelets, and Elastic Graph Matching (EGM). In addition, with this implementation it is possible to create and edit one´s tagged database, giving it a direct application. Also, a group of characteristics are proposed to enhance the functionalities of the program designed. Among these characteristics, three of them should be emphasized in this summary: A proposal of an hybrid verification mode of a biometric system; and an evaluation program capable of measuring, plotting curves, and comparing different configurations of each implemented recognition system; and a tool programmed to create personalized graphs and models (tagged graph associated to an image of a person), which can be used generally in object recognition. In the fourth and last part of the project, the results of the comparisons between different configurations of the systems implemented are shown for three databases (One of them created with pictures taken under non-controlled environments). The error rates of the proposed hybrid verification mode are measured as well. Finally, conclusions are extracted and future research studies are proposed.

Una codificación prefija para un idioma artificial

This work focuses in the formal and technical analysis of some aspects of a constructed language. As a first part of the work, a possible coding for the language will be studied, emphasizing the pre x coding, for which an extension of the Hu man algorithm from binary to n-ary will be implemented. Because of that in the language we can't know a priori the frequency of use of the words, a study will be done and several strategies will be proposed for an open words system, analyzing previously the existing number of words in current natural languages. As a possible upgrade of the coding, we'll take also a look to the synchronization loss problem, as well as to its solution: the self-synchronization, a t-codes study with the number of possible words for the language, as well as other alternatives. Finally, and from a less formal approach, several applications for the language have been developed: A voice synthesizer, a speech recognition system and a system font for the use of the language in text processors. For each of these applications, the process used for its construction, as well as the problems encountered and still to solve in each will be detailed.

Development of a Biometric Personal Authentication System Based on Fingerprint and Speech

Biometrics deals with the physiological and behavioral characteristics of an individual to establish identity. Fingerprint based authentication is the most advanced biometric authentication technology. The minutiae based fingerprint identification method offer reasonable identification rate. The feature minutiae map consists of about 70-100 minutia points and matching accuracy is dropping down while the size of database is growing up. Hence it is inevitable to make the size of the fingerprint feature code to be as smaller as possible so that identification may be much easier. In this research, a novel global singularity based fingerprint representation is proposed. Fingerprint baseline, which is the line between distal and intermediate phalangeal joint line in the fingerprint, is taken as the reference line. A polygon is formed with the singularities and the fingerprint baseline. The feature vectors are the polygonal angle, sides, area, type and the ridge counts in between the singularities. 100% recognition rate is achieved in this method. The method is compared with the conventional minutiae based recognition method in terms of computation time, receiver operator characteristics (ROC) and the feature vector length. Speech is a behavioural biometric modality and can be used for identification of a speaker. In this work, MFCC of text dependant speeches are computed and clustered using k-means algorithm. A backpropagation based Artificial Neural Network is trained to identify the clustered speech code. The performance of the neural network classifier is compared with the VQ based Euclidean minimum classifier. Biometric systems that use a single modality are usually affected by problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multifinger feature level fusion based fingerprint recognition is developed and the performances are measured in terms of the ROC curve. Score level fusion of fingerprint and speech based recognition system is done and 100% accuracy is achieved for a considerable range of matching threshold

Developing a Child Friendly Text-to-Speech System

This paper discusses the implementation details of a child friendly, good quality, English text-to-speech (TTS) system that is phoneme-based, concatenative, easy to set up and use with little memory. Direct waveform concatenation and linear prediction coding (LPC) are used. Most existing TTS systems are unit-selection based, which use standard speech databases available in neutral adult voices.Here reduced memory is achieved by the concatenation of phonemes and by replacing phonetic wave files with their LPC coefficients. Linguistic analysis was used to reduce the algorithmic complexity instead of signal processing techniques. Sufficient degree of customization and generalization catering to the needs of the child user had been included through the provision for vocabulary and voice selection to suit the requisites of the child. Prosody had also been incorporated. This inexpensive TTS systemwas implemented inMATLAB, with the synthesis presented by means of a graphical user interface (GUI), thus making it child friendly. This can be used not only as an interesting language learning aid for the normal child but it also serves as a speech aid to the vocally disabled child. The quality of the synthesized speech was evaluated using the mean opinion score (MOS).

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Low-resource language recognition using a fusion of phoneme posteriorgram counts, acoustic and glottal-based i-vectors

This paper presents a description of our system for the Albayzin 2012 LRE competition. One of the main characteristics of this evaluation was the reduced number of available files for training the system, especially for the empty condition where no training data set was provided but only a development set. In addition, the whole database was created from online videos and around one third of the training data was labeled as noisy files. Our primary system was the fusion of three different i-vector based systems: one acoustic system based on MFCCs, a phonotactic system using trigrams of phone-posteriorgram counts, and another acoustic system based on RPLPs that improved robustness against noise. A contrastive system that included new features based on the glottal source was also presented. Official and postevaluation results for all the conditions using the proposed metrics for the evaluation and the Cavg metric are presented in the paper.

Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech

We propose a study of the mathematical properties of voice as an audio signal -- This work includes signals in which the channel conditions are not ideal for emotion recognition -- Multiresolution analysis- discrete wavelet transform – was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states -- ANNs proved to be a system that allows an appropriate classification of such states -- This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features -- Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements.

Current research on sleep using experimental animals is limited by the expense and time-consuming nature of traditional EEG/EMG recordings. We present here an alternative, noninvasive approach utilizing piezoelectric films configured as highly sensitive motion detectors. These film strips attached to the floor of the rodent cage produce an electrical output in direct proportion to the distortion of the material. During sleep, movement associated with breathing is the predominant gross body movement and, thus, output from the piezoelectric transducer provided an accurate respiratory trace during sleep. During wake, respiratory movements are masked by other motor activities. An automatic pattern recognition system was developed to identify periods of sleep and wake using the piezoelectric generated signal. Due to the complex and highly variable waveforms that result from subtle postural adjustments in the animals, traditional signal analysis techniques were not sufficient for accurate classification of sleep versus wake. Therefore, a novel pattern recognition algorithm was developed that successfully distinguished sleep from wake in approximately 95% of all epochs. This algorithm may have general utility for a variety of signals in biomedical and engineering applications. This automated system for monitoring sleep is noninvasive, inexpensive, and may be useful for large-scale sleep studies including genetic approaches towards understanding sleep and sleep disorders, and the rapid screening of the efficacy of sleep or wake promoting drugs.

Speaker identification using models for phonemes

Motivation for Speaker recognition work is presented in the first part of the thesis. An exhaustive survey of past work in this field is also presented. A low cost system not including complex computation has been chosen for implementation. Towards achieving this a PC based system is designed and developed. A front end analog to digital convertor (12 bit) is built and interfaced to a PC. Software to control the ADC and to perform various analytical functions including feature vector evaluation is developed. It is shown that a fixed set of phrases incorporating evenly balanced phonemes is aptly suited for the speaker recognition work at hand. A set of phrases are chosen for recognition. Two new methods are adopted for the feature evaluation. Some new measurements involving a symmetry check method for pitch period detection and ACE‘ are used as featured. Arguments are provided to show the need for a new model for speech production. Starting from heuristic, a knowledge based (KB) speech production model is presented. In this model, a KB provides impulses to a voice producing mechanism and constant correction is applied via a feedback path. It is this correction that differs from speaker to speaker. Methods of defining measurable parameters for use as features are described. Algorithms for speaker recognition are developed and implemented. Two methods are presented. The first is based on the model postulated. Here the entropy on the utterance of a phoneme is evaluated. The transitions of voiced regions are used as speaker dependent features. The second method presented uses features found in other works, but evaluated differently. A knock—out scheme is used to provide the weightage values for the selection of features. Results of implementation are presented which show on an average of 80% recognition. It is also shown that if there are long gaps between sessions, the performance deteriorates and is speaker dependent. Cross recognition percentages are also presented and this in the worst case rises to 30% while the best case is 0%. Suggestions for further work are given in the concluding chapter.

A Prototype for a Multimodal Biometric Security system based on Face and Audio Signatures

Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared to individual unimodal recognition systems

Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

A System for Offline Recognition of Handwritten Characters in Malayalam Script

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets