Speech Recognizer

This paper identifies the different kinds of methods, which helps human to communicate with computer.
Traditionally the user interacting with computer via command line or GUI or common hand-held devices
such as mouse and keyboard which is not feasible for a handicapped or a blind or a mute people. In
order to interact with computer, the good interface required, the interface is sub part of human computer
Interaction (HCI). HCI not only software design, it also concentrates on design of hardware devices.
Describe the interaction between Brain and Computer based on EEG patterns. Brain generates EEG
signal Based upon that system has to act and it has to perform tasks like human in real world. Thus the
research work that is proposed would be to build a human to computer interaction through EEG signal.
The system that allow the user to interact directly brain to computer based EEG signal and no need to
use any input device to interact with any type of systems such are computer , TV’s. This way the computer
system can reduce the man work needed to feed information into the system by a large extent.

With the advent of technologies in mobile phones, many technological solutions have been implemented for visually impaired so that they can utilize them, and get benefited by them. Considering it as a key idea we have built an android application that will help blind people to send and read emails as ordinary people do. In this research paper we describe the VMAIL system architecture for android platform that can be used by a Blind Person to access e-mails easily. The application uses ‘text to speech’ and voice recognizer to facilitate sending, reading, forwarding and replying to emails using an android smart phone. The Application provides a secured platform by including an additional feature of Applock for accessing the application, which is designed considering the convenience of a blind person. KeywordsAppLock, Email, Speech Recognizer, Text to Speech, Vmail, Voice.

—The recognition of continuous speech is one of the main challenges in the building of automatic speech recognition (ASR) systems, especially when it comes to phonetically complex languages such as Arabic. An ASR system seems to be actually in a blocked alley. Nearly all solutions follow the same general model. The previous research focused on enhancing its performance by incorporating supplementary features. This paper is part of ongoing research efforts aimed at developing a high-performance Arabic speech recognition system for learning and teaching purposes. It investigates a statistical analysis of certain distinctive features of the basic Arabic phonemes which seems helpful in enhancing the performance of a baseline HMM-based ASR system. The statistics are collected using a particular Arabic speech database, which involves ten different male speakers and more than eight hours of speech which covers all Arabic phonemes. In HMM modeling framework, the statistics provided are helpful in establishing the appropriate number of HMM states for each phoneme and they can also be utilized as an initial condition for the EM estimation procedure, which generally, accelerates the estimation process and, thus, improves the performance of the system. The obtained findings are presented and possible applications of automatic speech recognition and speaker identification systems are also suggested.