Sharif Digital Repository

This thesis focuses on the problem of large vocabulary continuous speech recognition (LVCSR).Numerous research results in recent years proved effectiveness of deep neural networks (DNN) for LVCSR. As a result, many methods were proposed to incorporate DNNs in LVCSR. From one perspective we can look at these methods from the viewpoint of objective functions used for training DNNs. A frame-level objective function is one that is defined on frames locally, whereas a sequence-level objective function is defined on whole sequences. Since speech recognition is essentially a sequentional problem, here we focus on designing and imroving sequencelevel objective functions for DNNs. The main proposed... 

Nowadays, robustness of the Automatic Speech Recognition (ASR) systems against various noises is major challenge in these systems. Missing feature speech recognition approaches are our goal in this thesis for achieving robust ASR systems. In these approaches, low SNR regions of a spectrogram are considered to be “missing” or “unreliable” and are removed from the spectrogram. Noise compensation is carried out by either estimating the missing regions from the remaining regions in some manner prior to recognition, or by performing recognition directly on incomplete spectrograms. These techniques clearly require a "spectrographic mask" which accurately labels the reliable and unreliable regions... 

Currently, in the Islamic Republic of Iran, after aviation accidents and incidents, conversations between pilots and air traffic controllers are re-examined by the State Air Transport Organization of the Islamic Republic of Iran and turned into text. The Automatic Recognition System for Pilot-Air Traffic Service Units’ Communication helps in the implementation of speech recognition. Reducing the time and cost of converting conversations into texts and creating an aviation database in the country are other uses of this system. In this research, after collecting and refining the actual conversation between pilots and air traffic controllers and examining seven methods, we design a system that... 

During recent years, neural networks have been used for language modeling in tasks related to natural language processing. In these models, various structures of neural networks have been used, and recurrent networks (RNN) have achieved good results in these tasks. Since RNNs are not limited to a fixed number of words for predicting next word, they have achieved better results than feedforward networks. However, these networks have problems to learn long sequences, and long short-term memory (LSTM) networks have been presented for solving this problem. In this research, language models are extracted for Persian language using RNN and LSTM, and are compared with n-gram-based models. For... 

This thesis is focused on the spoken term detection system based on speech recognition in low resources conditions. A spoken term detection system is composed of two parts: speech recognition and search. In search of words, the method of proxy words is used as a basic approache to overcome the problem of OOV words. The main challenge in this thesis in the context of low resources, is poor training acoustic and language models and the small lexicon in speech recognition. Small lexicon increases the number of OOV words. In this thesis, two innovation has been proposed to improve the basic system. The first is training a bottleneck neural network for extraction the articulatory features of... 

Generally speaking a web browser is a software application for surfing the World Wide Web. A user with web browser can request some web pages on the Internet. This request would be sent to web server and would be analyzed. The result would be shown to end user by web browser GUI. A web browser has different parts such as HTML parser, Renderer, browser engine and GUI. The GUI is one of the most important parts of each web browser, because the end users interact with GUI. The classical GUI for surfing has been used in various platforms, such as the PC and Laptop Operating systems. Because of the technological advances and the introduction of tablets and other touch screen devices i. e, smart... 

In this dissertation, we introduced a Fuzzy based representation and comparison method for sequential patterns such as speech and online handwriting. The new model, called Fuzzy Elastic Matching Machine (FEMM), is simpler than traditional HMM based approaches and is not based on the common statistical assumptions of HMM systems. The model was tested on isolated word and phoneme recognition tasks in speech recognition domain and isolated letter recognition in Persian handwriting recognition. We showed that this method is faster than traditional HMM based models and more robust to noise. To train the model, we introduced a Symbiogenesis-based evolutionary training algorithm. This algorithm... 

Automatic Speech Recognition (ASR) systems are a significant part of Personal Assistants in mobile phones. But because of the time-dependent nature of ASR systems, they are computation and memory-intensive tasks. On the other hand, mobile devices utilize a Low-Power design to extend battery life and improve user experience, making them incompatible with heavy-loaded tasks such as ASR systems. For instance, if we run an inference with a 60 seconds audio file on a well-known open-sourced Speech Recognition System named DeepSpeech, it will only take 49 seconds for a desktop PC to generate the results. Still, a mobile phone with ARM64 architecture with the same input file will take 92 seconds to...