Character-Level Incremental Speech Recognition with Recurrent Neural Networks

In real-time speech recognition applications, the latency is an important issue. We have developed a character-level incremental speech recognition (ISR) system that responds quickly even during the speech, where the hypotheses are gradually improved while the speaking proceeds. The algorithm employs a speech-to-character unidirectional recurrent neural network (RNN), which is end-to-end trained with connectionist temporal classification (CTC), and an RNN-based character-level language model (LM). The output values of the CTC-trained RNN are character-level probabilities, which are processed by beam search decoding. The RNN LM augments the decoding by providing long-term dependency information. We propose tree-based online beam search with additional depth-pruning, which enables the system to process infinitely long input speech with low latency. This system not only responds quickly on speech but also can dictate out-of-vocabulary (OOV) words according to pronunciation. The proposed model achieves the word error rate (WER) of 8.90% on the Wall Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284 training set.Comment: To appear in ICASSP 201

Розробка автоматизованої системи розпiзнавання символiв

Проведено аналiз дослiдження областi розпiзнавання символiв. Спроектовано та розроблено автоматизовану систему розпiзнавання символiв, яка виконує наступнi функцiї: автоматизоване розпiзнавання символiв; мо- жливiсть конструювання нових нейронних мереж для розпiзнавання рiзних образiв; зберiгання необмеженої кiлькостi нейронних мереж; можливiсть iнтеграцiї з iншими програмними продуктами.The analysis of the study area character recognition. Designed and developed automated system for character recognition, which performs the following functions: automatic character recognition, the possibility of constructi- ng new neural networks for recognition of different images, store an unlimited number of neural networks, the ability to integrate with other software

Deep Learning Architectures for Novel Problems

With convolutional neural networks revolutionizing the computer vision field it is important to extend the capabilities of neural-based systems to dynamic and unrestricted data like graphs. Doing so not only expands the applications of such systems, but also provide more insight into improvements to neural-based systems. Currently most implementations of graph neural networks are based on vertex filtering on fixed adjacency matrices. Although important for a lot of applications, vertex filtering restricts the applications to vertex focused graphs and cannot be efficiently extended to edge focused graphs like social networks. Applications of current systems are mostly limited to images and document references. Beyond the graph applications, this work also explored the usage of convolutional neural networks for intelligent character recognition in a novel way. Most systems define Intelligent Character Recognition as either a recurrent classification problem or image classification. This achieves great performance in a limited environment but does not generalize well on real world applications. This work defines intelligent Character Recognition as a segmentation problem which we show to provide many benefits. The goal of this work was to explore alternatives to current graph neural networks implementations as well as exploring new applications of such system. This work also focused on improving Intelligent Character Recognition techniques on isolated words using deep learning techniques. Due to the contrast between these to contributions this documents was divided into Part I focusing on the graph work, and Part II focusing on the intelligent character recognition work

English character recognition algorithm by improving the weights of MLP neural network with dragonfly algorithm

Character Recognition (CR) is taken into consideration for years. Meanwhile, the neural network plays an important role in recognizing handwritten characters. Many character identification reports have been publishing in English, but still the minimum training timing and high accuracy of handwriting English symbols and characters by utilizing a method of neural networks are represents as open problems. Therefore, creating a character recognition system manually and automatically is very important. In this research, an attempt has been done to incubate an automatic symbols and character system for recognition for English with minimum training and a very high recognition accuracy and classification timing. In the proposed idea for improving the weights of the MLP neural network method in the process of teaching and learning character recognition, the dragonfly optimization algorithm has been used. The innovation of the proposed detection system is that with a combination of dragonfly optimization technique and MLP neural networks, the precisions of the system are recovered, and the computing time is minimized. The approach which was used in this study to identify English characters has high accuracy and minimum training time

Performance Analysis of Handwritten Marathi Character Recognition with RBF, Cascade, Elman and Feed Forward Neural Networks

Character recognition of handwritten Marathi curve scripts is one of the most challenging areas of research in neural networks due to high variability in writing styles. Marathi characters have shirolekhas and spines. This seriously affects many of the performance recognition parameters and much more.In this paper, we are performing the performance analysis of RBF neural network, Cascade Neural network, Elman Neural network and Feed forward neural network for the character recognition of handwritten Marathi curve scripts. For the experiment, we have taken in to account the six samples each of 48 Marathi characters. For every sampled character, the �Edge detection and dilation method of Feature extraction�with a set of image pre-processing operations have been performed. Here to study and analyze the performance of these four neural networks, firstly we have created the network, trained the network, simulated the network and plotted the regression plots. It has been analyzed that RBF neural networks has a high regression value as compared to the rest of the methods for the training set