This paper focuses on the adaptation of Automatic Speech Recognition systems using Hybrid models combining Artificial Neural Networks with Hidden Markov Models. A classical adaptation technique consists in adding a linear transformation network that acts as a pre-processor to the main network. We investigated the application of linear transformations not only to the input features, but also to ...

Data warehousing is gaining importance day by day in enterprises, as it helps them to improve their business intelligence. The process of creating a data warehouse needs to be automated so that the transactional sources are generated in least time, with maximum accuracy and with minimum dependability on users. This automation proves its worth particularly when working with small and medium ente...

Artificial neural networks (ANN) have proven to be well suited to the task of articulatory feature (AF) recognition. However, one drawback with an ANN approach is that features are assumed to be statistically independent. We address this by using ANNs to provide virtual evidence to a dynamic Bayesian network (DBN). This gives a hybrid ANN/DBN model and allows modelling of inter-feature dependen...

This paper introduces a real-time system for verifying handwritten signatures that relies on a hybrid methodology, for which consistency checking is performed prior to enrolling signatures for further processing. Only the best six signatures are retained out of 10 signatures for each signer, during the enrollment phase, based on the deviation in both the total signing time and the binary patter...

This paper presents a new approach to speech recognition with hybrid HMM/ANN technology. While the standard approach to hybrid HMM/ANN systems is based on the use of neural networks as posterior probability estimators, the new approach is based on the use of mutual information neural networks trained with a special learning algorithm in order to maximize the mutual information between the input...

Most machine learning algorithms are sensitive to class imbalances of the training data and tend to behave inaccurately on classes represented by only a few examples. The case of neural nets applied to speech recognition is no exception, but this situation is unusual in the sense that the neural nets here act as posterior probability estimators and not as classifiers. Most remedies designed to ...

INTRODUCTION Recent advances in the applications of ANN have demonstrated successful cases in time series analysis, data mining, civil engineering, financial analysis, music creation, fishing prediction, production scheduling, intruder detection, etc., making them an important tool for research and development[1]. ANN and evolutionary computation(EC) techniques have been employed successfully i...

Artificial neural networks (ANNs) are widely used in various applications such as recognition, security, computer learning and so on. To meet requirements of higher performance, hardware implementations have been widely researched and developed. The popular implementation methods are FPGA, analog, digital and hybrid methods. The FPGA method is widely used due to the low cost and short design ti...

This paper describes the recently developed artificial neural network (ANN) modules in HTK hidden Markov model toolkit, which enables ANN models with very general feed-forward architectures to be used for either acoustic modelling or feature extraction. The HTK ANN extension includes many recent ANN-based speech processing techniques, such as sequence training, model stacking, speaker adaptatio...

Social networking sites Twitter is frequently used as a platform for information gathering various communities/forums well individuals to discuss certain things. Dissemination of on can be in the form positive and negative information. One hate speech contained hashtags twitter. Hate Speech Hashtag Classification was carried out using Hybrid Artificial Neural Network (ANN) method produce satisf...