Based on machine learning scheme to develop a smart robot embedded with GMM-UBM

Smart Robot embedded with GMM-UBM (Gaussian mixture model- universal background model) based on the machine learning scheme is presented in the article. Authors have designed a smart robot for the farmer and which is designed controlled by the concept of machine learning. On the other hand, the techniques of machine learning are applied to develop a smart robot for helping farmers recognize the environment conditions, e.g. weather, and disease protection in rice or plant. The smart robot is implemented to detect and to recognize the environment conditions around a fixed area. The sensing way through vision devices, such as camera, look like a human’s eye to distinguish various types of target. The QR code is deployed to simulate working conditions allows the robot to separate conditions and act according to conditions precisely. Besides, the smart robot is embedded with GMM-UBM algorithm for promoting the accuracy of recognition from the deployment of machine learning. The smart robot, mainly combines with AI (Artificial intelligence) techniques, consists of the following equipments: 1) a control movement subsystem, 2) a sensor control subsystem, and 3) an analysis subsystem. The researcher has determined the condition of the message options via QR code. In addition, the contents of the QR code tag will be processed a text message and saved to a memory device, once the reading is finished. The data analysis subsystem then reads the text and recommends the robot to move according to the specified conditions. The results from QR code data allow the smart robot to accurately collect many kinds of prefer data (e.g., climate data) in the farm at the specified location.

Identificación automática de transformación en el bosque seco tropical colombiano usando GMM y UBM-GMM

Hoy, los métodos de aprendizaje automático se han convertido en una herramienta para ayudar a frenar los efectos del calentamiento global, al resolver cuestiones ecológicas. En particular, el bosque seco tropical (BST) de Colombia se encuentra actualmente amenazado por la deforestación generada, desde la época colonial, por la ganadería, la minería y el desarrollo urbano. Uno de los desafíos urgentes en esta área es comprender la transformacion y degradación de los bosques. Tradicionalmente, los cambios de los ecosistemas se miden por varios niveles de transformación (alto, medio, bajo). Estos se obtienen a través de observación directa, recuento de especies y medidas de variación espacial a lo largo del tiempo. Por ende, estos métodos son invasivos y requieren de largos lapsos de observación en los lugares de estudio. Una alternativa eficaz a los métodos clásicos es el monitoreo acústico pasivo, que es menos invasivo, ya que evita el aislamiento de las especies y reduce el tiempo de los investigadores en los sitios. Sin embargo, implica la generación de múltiples datos y la necesidad de herramientas computacionales destinadas al análisis de las grabaciones. Este trabajo propone un método para identificar automáticamente la transformación del BST mediante grabaciones acústicas, aplicando dos modelos de clasificación: Gaussian Mixture Models (GMM), por cada región estudiada, y Universal Background Model (UBM), para un modelo general. Además, contiene un análisis de índices acústicos, con el fin de detectar los más representativos para las transformaciones del BST. Nuestra propuesta de GMM alcanzó una precisión de 93% y 89% para las regiones de La Guajira y Bolívar. El modelo general UBM logró 84% de precisión.

Language Identification based on Support Vector Machine using GMM Super vectors

This paper proposes a novel approach that combines the power of generative Gaussian mixture models (GMM) and discriminative support vector machines (SVM). The main objective this paper is to incorporating the GMM super vectors based on SVM classifier for language identification (LID) task. The GMM based LID system to capture all the variations present in phonotactic constraints imposed by the language requires large amount of training data. The Gaussian mixture model (GMM)- universal background model (UBM) modeling require less amount of training data. In GMM-UBM LID system, a language model is created by maximum a posterior (MAP) adaptation of the means of the universal background model (UBM). Here the GMM super vectors are created by concatenating the means of the adapted mixture components from UBM. Then these super vectors are applied to a SVM for classification purpose. In this paper, the performance of GMM-UBM LID system based on SVM is compared with the conventional GMM LID system. Form the performance analysis it is found that GMM-UBM LID system based on SVM is performed well when compared to GMM based LID system.

GMM-UBM Based Modeling for Language Identification using New Feature Vectors

The most of the existing LID systems based on the Gaussian Mixture model. The main requirement of the GMM based LID system is it require large amount of speech data to train the GMM model. Most of the Indian languages have the similarity because they are derived from Devanagari. Even though common phonemes exists in phoneme sets across the Indian languages, each language contain its unique phonotactic constraints imposed by the language. Any modeling technique capable of capturing all these slight variations imposed by the language is one of the important language identification cue. To model the GMM based LID system which captures above variations it require large number of mixture components.To model the large number of mixture components using Gaussian Mixture Model (GMM), the technique requires a large number of training data for each language class, which is very difficult to get for Indian languages. The main objective of GMM-UBM based LID system is it require less amount of training data to train(model) the system. In this paper, the importance of GMM-UBM modeling for language identification (LID) task for Indian languages are explored using new set of feature vectors. In GMM-UBM LID system based on the new feature vectors, the phonotactic variations imparted by different Indian languages are modeled using Gaussian Mixture model and Universal Background Model (GMM-UBM) technique. In this type of modeling, some amount of data from each class of language is pooled to create a universal background model. From this UBM model each model class is adapted. In this study, it is found that the performance of new feature vectors GMM-UBM based LID system is superior when compared to conventional new feature vectors based GMM LID system.

Spoofing Detection and Countermeasure in Automatic Speaker Verification System using Dynamic Features

This present paper aims to extract robust dynamic features used to spoofing detection and countermeasure in ASV system. ASV is a biometric person authentication system. Researchers are aiming to develop spoofing detection and countermeasure techniques to protect this system against different spoofing attacks. For this, replayed attack is considered, because of very common accessibility of recording devices. In replay spoofing, the speech utterances of target (genuine) speakers are recorded and played against ASV system for gaining access unauthorizedly. For this purpose, as a first step, different dynamic features will be extracted for each speech sample. For feature extraction MFCC, LFCC, and MGDCC feature extraction techniques are used. As a second step, a classifier is used to classify whether the given speech sample is genuine or not. As a classifier, GMM and universal background model is used. In this present work, GMM based ASV system and Countermeasure systems using different feature extraction techniques are developed, and the performance of the methods is evaluated using EER and t- DCF. Basing on the performance values, the best feature extraction technique is selected.