Report on Voice Recognition using MATLAB

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Voice recognition software is a computer program that can understand human speech and convert it into easy-to-read text. Speech recognition software cannot only understand human voice, but also use human voice information to perform tasks more accurately. Daily Speech Recognition software application includes voice activation assistants such as Alexa and Siri that perform simple tasks according to voice commands.

Journal 4 Research - J4R Journal, 2017

Voice recognition system is a system which is used to convert human voice into signal, which can be understood by the machines. When this is achieved, the machine can be made to work, as desired. The machine could be a computer, a typewriter, or even a robot. There are systems available, in which the machine 'speaks' the recorded word. But that is out of the scope of this paper. Here, only the human is expected to talk. Further, the voice recognition systems described here, can be used for projects only.

Speech recognition applications include voice user interfaces such as voice dialing, simple data entry, p reparation of structured documents, speech-to-text processing, and aircraft. The term voice recognition or speaker identification refers to identifying the speaker, rather than what they are saying. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on a specific person's voice or it can be used to authenticate or verify the identity of a speaker as part of a security process.

International Journal of Engineering & Technology, 2018

This scientific work concerning an examination on automatic speech recognition (ASR) frameworks connected with the home automation and to express the importance of this academic work, an itemized investigation of the engineering of speech recognition frameworks was completed. Our goal in Information Systems Engineering Research Group ofAbdelmalekEssaadi University is to choose a speech recognition programming that must work in remote speech conditions and in a rowdy area.The proposed framework is using atoolbox called Kaldi, which must correspond as aclient created by an advanced programming language, with any home automation framework.

Speech recognition is one of the next generation technologies for human-computer interaction. Speech recognition has been researched since the late 1950s but due to its computational complexity and limited computing capabilities of the last few decades, its progress has been impeded. In laboratory settings automatic speech recognition systems (ASR) have achieved high levels of recognition accuracies, which tend to degrade in real world environments. This paper analyses the basics of the speech recognition system. Major problems faced by ASR in real world environments have been discussed with major focus on the techniques. These technique used in the development of noise robust ASR .

2016

BACKGROUND In typical speech recognition systems, a computing device, such as a mobile computing device (smart phone, tablet, etc.), will receive a speech input from a user. The speech input can be received via a microphone of the computing device and converted to an audio signal for processing. The audio signal is parsed and otherwise processed to recognize speech utterances, which can be output as text and/or utilized by the computing device to perform a function (for example, voice search, email or text message dictation, or other command). A user may not, however, utilize the full speech capabilities of her/his computing device in public spaces. For example only, a user may choose to avoid providing speech inputs in public places due to noisy conditions, which may degrade the performance of the speech recognition system, and/or to avoid providing a relatively loud speech input due to privacy concerns, whether actual or perceived. It would be desirable to provide an improved speech recognition system and method that provides better performance in noisy conditions while also providing increased privacy for the user.

ost of us frequently use speech to communicate with other people. Most of us will also communicate regularly with a computer, but rarely by means of speech. The computer input usually comes from a keyboard or a mouse, and the output goes to the monitor or a printer. Still, in many cases the communication with a computer would be facilitated if speech could be used, if only because most people speak faster than they type. A necessary requirement for this is that the computer is able to recognise our speech: automatic speech recognition (ASR).

This work provide mathematical insight to Automatic Speech Recognition (ASR) system’s algorithm such that, the intricacy of the system becomes a simplified correlation of the ASR algorithm to the physical form using the mathematical flowchart which clearly and uniquely show the link from one stage of the algorithm to the other. The mathematical profile of the ASR algorithm starts from the data input module, through noise cancellation module, voice activity detection module, pre-processing module, Linear Predictive Coding (LPC) based feature extraction module, then provides alternate root for both Dynamic Time Wapping (DTW) and Hidden Markov Model (HMM) based pattern matching module after which the output is fed to the final decision module of the ASR algorithm. The modern research outputs has improved the robustness of each stage of the algorithm but the approach used here focused on the basics of each stage which helps in easy and better understanding of the ASR system. It also aid in the evaluation and create necessary intuition for decoding problems of the recent ASR systems for new researchers in the research area.

Underlying of speech data refers the speaker features which are useful in speech recognition, speech processing, speech coding, and speech clustering. We described a brief of the area of speaker recognition, speech applications, and their underlying techniques. The review of automatic speech recognition (ASR) will discuss some of the positive and negative aspects of speaker recognition technologies and also outline the potential trends in research, development and applications.

International Journal of Creative Research Thoughts (IJCRT), 2023

Speech recognition is the process of converting human sound signals into words or instructions. The research of speech recognition involves many subject areas such as computer technology, artificial intelligence, digital signal processing, pattern recognition, acoustics, linguistics, and cognitive science.Our speech is made up of many frequencies at the same time. The actual signal is really a sum of all those frequencies stuck together. The conversation or speech that is captured by a microphone or a telephone is converted from acoustic signal to a set of words in speech recognition.