Speech to Text Wireless Converter

Transcription

1 Speech to Text Wireless Converter Kailas Puri 1, Vivek Ajage 2, Satyam Mali 3, Akhil Wasnik 4, Amey Naik 5 And Guided by Dr. Prof. M. S. Panse 6 1,2,3,4,5,6 Department of Electrical Engineering, Veermata Jijabai Technological Institute (VJTI) 1-6, Mumbai, Maharashtra- India. Abstract This paper presents the design of the low-cost voice recognition based home automation system for the physically challenged people who cannot move their limbs but can speak and listen. By using our system these people will be able to control the various home appliances by voice commands, also they will be able to give some commands to other people for their own help by displaying words on the LCD. The trained voice recognition module is used to recognize voice commands. The system acquires speech at run time through a microphone and processes the sampled speech to recognize the uttered words. Voice Recognition Module converts the Voice signal to text output. The recognized text can be stored in module that is connected to an Arduino. Index Terms Wireless network, Automatic Voice Recognition, Speech Synthesis, Arduino Uno. I. INTRODUCTION Electronics and technology is growing fast and along with improvement in our daily luxurious life, It is also helpful for differently abled persons. Nearly 20% people of the world are suffering from various disabilities and Technology can be used for betterment of same. It has a number of applications in different areas and provides potential benefits. Speech recognition reduces the difficulties and problems caused by other communication methods. In the past speech has not been used much in the field of electronics and computers. However, with modern processes, algorithms, and methods we can process speech signals easily and use it in our desirable fields. Speech-to-text engine directly converts speech to text. It can complement the idea giving users a different choice for data entry. Speech-to-text engine can also provide data entry options for blind, deaf, or physically handicapped users. Our project is capable to recognize the speech and convert the input audio into text (analog input to digital output to perform various actions); it also enables a user to perform operations by providing voice input. II. ALGORITHMS Algorithms of involved in the implemented system are described below: 1. Type of Speech: Speech recognition system can be separated in different classes by describing what type of utterances they can recognize. 1.1 Isolated Word: Isolated word recognizes attain usually require each utterance to have quiet on both side of sample windows. It accepts single words or single utterances at a time. This is having Listen and Non-Listen state. Isolated utterance might be better name of this class. 1.2 Connected Word: Connected word system are similar to isolated words but allow to divide or separate sound to be run together minimum pause between them. 1.3 Continuous speech: Continuous speech recognizers allows user to talk almost naturally, while the computer determine the content. Recognizer with continues speech capabilities are some of the most difficult to create because they utilize unique sound and special method to determine utterance boundaries. 1.4 Spontaneous speech: At a basic level, it can be thought of as speech that is natural sounding and not rehearsed. An Automatic Speech Recognition (ASR) System with spontaneous speech ability should be able to handle a different words and variety of natural speech feature such as words being run together. 2. Types of Speaker Model: All speakers have their special voices, due to their unique physical body and personality. Speech recognition system is broadly classified into main categories based on speaker models, namely, speaker dependent and speaker independent. 2.1 Speaker independent models: Speaker independent systems are designed for variety of speakers. It IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 225

2 recognizes the speech patterns of a large group of people. This system is most difficult. 2.2 Speaker dependent models: Speaker dependent systems are designed for a specific speaker. These systems are usually easier to develop, cheaper and more accurate, but not as flexible as speaker adaptive or speaker independent systems. They are generally more accurate for the particular speaker, but much less accurate for others speakers. 3. Speech to Text System: Voice Recognition Module is a compact and easycontrol speaking recognition board. This is a speakerdependent voice recognition module. It supports up to 80 voice commands in all. Max 7 voice commands could work at the same time. Any sound could be trained as command. Users need to train the module first before starts recognizing any voice command. On Module, voice commands are stored in memory in the form of library. Any 7 voice commands in the library could be imported into recognizer. It means 7 commands are effective at the same time. or models that characterize the statistical properties of pattern. Figure 2.2 Training Phase B) Recognizing Phase:During recognizing phase an unknown input pattern, is identified by considering the set of references. In this voice recognition approach, input is provided by speaker which is then passed through recognizer module. it processes the analog sound and generate the digital output. This digital output is compared with dictionary values and system checks if any match is there. If closest match is found then it generates output and displays word and its meaning at output. If values are not matching the then whole process restarts. Figure 2.1 Speech to Text system 4. Automatic Voice Recognition: 4.1 Phase of systems: This Systems operate in two phases as follows. A) Training Phase: During training the system learns the reference patterns representing the different voice sounds (e.g. phrases, words) that constitute the vocabulary of the application. Each reference is learned from spoken examples and stored either in the form of templates obtained by some averaging method Figure 2.3 Algorithm for recognizing phase IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 226

3 The speech input from microphone is given to the voice recognition module where the speech signal is compared with the previously stored trained voice samples. Upon successful recognition of voice command at the transmitter side the Arduino microcontroller actuates the corresponding electrical device like turning on lights and sends the data to receiver. At the receiver side Arduino displays the input word on LCD and according to this word action is taken and a signal is fed to relay to control appliances. The buzzer beeps when differently abled person is in need of help or some assistance. IV. CIRCUIT DIAGRAM Figure 2.4 Combined block diagram of above 2 phases This approach of speech recognition system consists of five blocks-feature extraction, Acoustic modeling, Pronunciation modeling, Decoder. The process of speech recognition begins with a speaker creating an utterance which consists of the soundwaves. These sound waves are then captured by a microphone and converted into electrical signals. These electrical signals are then converted into digital form to make them understandable by the speech-system. Speech signal is then converted into discrete sequence of feature vectors, which is assumed to contain only the relevant information about given utterance that is important for its correct recognition. III. SYSTEM OVERVIEW The voice recognition based home automation system is an integrated system to facilitate the elderly and physically challenged people with an easily operated home automation system that operates fully on voice commands. The functional block diagram of this system is shown: Figure 4.1 Circuit Diagram of Transmitter Figure 3.1 Schematic diagram of speech to text Figure 4.2 Circuit Diagram of Receiver. IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 227

4 V. SYSTEM HARDWARE 1. Arduino UNO: Arduino is heart of this project. It works as interface between voice recognition module and output devices. The recognized speech is accepted by Arduino Uno and then corresponding control signal can be sent to the respective relays The Arduino platform was providing an inexpensive and easy way for students and professionals to create devices that interact with their environment using sensors and actuators. The Arduino microcontroller is based on the ATmega 328. It has 14 digital input/output pins (Out of these 14 pins 6 can be used as PWM outputs) and 6 analog inputs. Arduino works on 5V D.C and has clock speed of 16 MHz. Electromagneticinduction. Figure 5.2 Relay Circuit 3. Bluetooth Module HC 05/06: Bluetooth is wireless technology standard for exchanging data over short distances. In our system, they are used for extension of range of the operation. They are present on both transmitter and receiver side This module is connected to Arduino that sends out signals to its receiver unit through Bluetooth. These modules on both transmitter and receiver side needs to be paired with each other. Once they are paired other interferences are avoided and rejected. Data is converted from speech to text that is transmitted and received through these units. Figure 5.1 Arduino Uno 2. Buzzer Buzzer is main indicator of the implemented system through which the guardians of the differently abled people can be alerted to keep eye on them when buzzer rings and take necessary care. If the patient needs any help then by voice command he or she may turn on the buzzer for help. 3. Relay Module To control the Home appliances relays are used with the Arduino.. The relays used in the system are 5V-5 pin relay as shown in Fig. 4. The relay remains in normally closed state. When relay coils are energized the relay switches from normally closed to normally open state due to the VI. Figure 5.3 Bluetooth unit HC 05 and HC 06 CONCLUSION AND FUTURE SCOPE This paper highlights the features of Speech to Text System Converter unit which is low cost and user friendly solution for the problems faced by differently abled persons and patients. This unit can also be implemented as singular unit in our day to day life for luxurious purposes. can also be implemented to improve pronunciation and implement it in educational zone. IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 228

5 Greater use will be made of intelligent systems which will attempt to guess what the speaker intends to speak, rather than what was actually said, as people often misspeak and make unintentional mistakes. Microphone and sound systems will be designed to adapt more quickly to changing background noise levels, different environments. RESULTS Figure 7.1 Transmitting and Receiving data Engineering Research and Applications (IJERA) ISSN: Vol. 2, Issue 3, May-Jun [2] Li Deng, IEEE Transactions on Audio, Speech, and Language Processing IEEE Signal Processing Society, vol. A247, pp , Apr [3] Ms. Sneha K. Upadhyay,Mr. Vijay N. Chavda, Intelligent system based on speech recognition with capability of self learning,international Journal For Technological Research In Engineering ISSN (Online): Volume 1, Issue 9, May [4] Deepa V.Jose, Alfateh Mustafa, Sharan R, A Novel Model for Speech to Text Conversion International Refereed Journal of Engineering and Science (IRJES)ISSN (Online) X, Volume 3, Issue 1 (January 2014). [5] Olabe, J. C.; Santos, A.; Martinez, R.; Munoz, E.; Martinez, M.; Quilis, A.; Bernstein, J., Real time text-tospeech conversion system for spanish," Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '84., vol.9, no., pp.85,87, Mar [6] Parvinder Pal Sing., Speech recognition as emerging revolutionary technology " (IJARCET), vol.5, no., pp.55,67, Mar [7] Susanne Wagner (Halle), Speech to text in real time : challenges and opportunities " MuTra 2005 Challenges of Multidimensional Translation: Conference Proceedings,no., pp.6, 7, Mar Figure 7.2 Prototype Setup of Device The System allows differently abled people to access different device through their speech. This system is practically implemented and thus the results are obtained. Results of this system are as shown above figures. Fig 7.1 shows transmitting and receiving data of system and Fig. 7.2 shows the setup of system with detected word LIGHT to turn on the light. REFERENCE [1] Sanjib Das, Speech Recognition Technique: A Review,International Journal of IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 229