Recent advances in LVCSR : A benchmark comparison of performances

Large Vocabulary Continuous Speech Recognition (LVCSR), which is characterized by a high variability of the speech, is the most challenging task in automatic speech recognition (ASR). Believing that the evaluation of ASR systems on relevant and common speech corpora is one of the key factors that help accelerating research, we present, in this paper, a benchmark comparison of the performances of the current state-of-the-art LVCSR systems over different speech recognition tasks. Furthermore, we put objectively into evidence the best performing technologies and the best accuracy achieved so far in each task. The benchmarks have shown that the Deep Neural Networks and Convolutional Neural Networks have proven their efficiency on several LVCSR tasks by outperforming the traditional Hidden Markov Models and Guaussian Mixture Models. They have also shown that despite the satisfying performances in some LVCSR tasks, the problem of large-vocabulary speech recognition is far from being solved in some others, where more research efforts are still needed

Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition

This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network

Modern Speech Identification Model using Acoustic Neural approach

Modern day technology demands sophisticated technology to give input commands to computational devices. Prominent techniques has to be introduced to make human machine interface smooth and compatible especially speech signals. Establishing efficient communication between computer and machine plays a vital role in speech processing. This article uses one of current technologies in the Continuous Speech Recognition systems which is Reservoir Computing based Neural Network followed by likelihood conversion. Our aim is to build a stand-alone system which understands the terminology of languages. Throughout the development, measures will be taken to keep the memory requirement and the processing time of the software as small as possible