1. INTRODUCTION Speech is the most effective mode of communication used by humans. Automatic speech recognition can be defined as a technology which enables a system to recognize the input speech signals and interpret the meaning, after which the system should be able to generate some control signals. 1.1 AIM Aim of this project is to realize an Automatic Speech Recognition system in hardware which is able to understand limited Malayalam words spoken into the microphone. The system works well in room environment (approximately 20dB SNR). For the proper functioning of the system, there should be distinct pauses between the words i.e. isolated words. Due to the memory constrains in the handheld device, the vocabulary supported by the system is limited i.e. it is a limited vocabulary speaker independent isolated word recognition system. 1.2 OBJECTIVE The first phase of this work is to simulate the system which recognizes limited Malayalam numerals from one to six in PYTHON. In order to increase the accuracy of recognition, a pre-processing technique called voice activity detection, which detect the start and end points of a words, needs to be implemented. In the second phase its hardware implementation has to be done in RASPBERRY PI. 2. LITERATURE REVIEW Nowadays, innovation in scientific research is focused much more on the interactions between humans and technology and automatic speech recognition is a driving force in this process. Speech recognition technology is changing the way information is accessed, tasks are accomplished and business is done. Automatic speech recognition (ASR) is the ability of a machine to convert spoken language to recognized words. 2.1 TYPES OF ASR SYSTEMS ASR can be classified in sever... ... middle of paper ... ...CHART REFERENCES [1] S. Grassi, M.Ansorge et al, “Implementation of Automatic Speech Recognition for Low-Power Miniaturized Devices”, Published in Proceedings of the 5th COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, 2003, pp.59-64 [2] Andrew Carl Lindgren B.S, “Speech Recognition Using Features Extracted from Phase Space Reconstructions”, Marquette University, Milwaukee, Wisconsin, May 2003 [3] Shivanker Dev Dhingra , Geeta Nijhawan , Poonam Pandit , “Isolated speech recognition using mfcc and dtw”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 8, August 2013 [4] M.A.Anusuya, S.K.Katti, “Classification Techniques used in Speech Recognition Applications: A Review”, International Journal of computer applications, Vol. 2, AUGUST 2011, pp.910-954
There are many everyday devises that we hearing people take for granted, among these are telephones, smoke alarms, doorbells, and alarm clocks. When we look at how members of the deaf community use these everyday items we must consider that members within the community have very different communication needs, abilities, and preferences. Hard-of-hearing people for example can use a standard telephone with the addition of a headset or amplifier, while some hard-of-hearing people may prefer a TTY deaf persons rely on it, or a relay service to communicate as we (hearing people) would on a telephone.
Human conversations are too complicated for machines to understand and interact properly without flaws. This is what separates humans from animals. Even the dumbest man will be able to form sentences and converse with other human beings, while even the smartest animals will never be able to.... ... middle of paper ... ...
The American public has had a craving for less social contact as the millennia continues to wane, and Siri-Speech is the perfect solution for this need. The average adolescent American sends approximately 88 text messages per day, which is decent but still requires improvement, as they still have to drudge through the burden that is sounds uttered with vocal cords. Although speech has been less arduous in the modern era, with the clever use of acronyms like LOL, TTYL and ILY, there are many other tedious phrases that still need to be sounded out every single day. Siri-Speech addresses this problem as well by converting every single phrase into an Acronym to heighten convenience for the user, so that they can get back to important measures like browsing videos of funny cats on YouTube. For example, a phrase previously spoken as “I have to go. I will see you tonight at the movie theatre” is now spoken as “I have to go,” which is truly the epitome of efficiency and progre...
Automatic speech recognition is the most successful and accurate of these applications. It is currently making a use of a technique called “shadowing” or sometimes called “voicewriting.” Rather than have the speaker’s speech directly transcribed by the system, a hearing person whose speech is well-trained to an ASR system repeats the words being spoken.
American Speech-Language-Hearing Association. American Speech-Language-Hearing Association. Web. The Web. The Web.
In the partial alphabetic phase individuals pay attention to different letters in a word in order to attempt its pronunciation, usually the first and final letters of a word are focused on, Ehri referred to this as ‘phonetic cue reading’. This is a skill which along with others which shows phonological awareness.
Hearing loss is often overlooked because our hearing is an invisible sense that is always expected to be in action. Yet, there are people everywhere that suffer from the effects of hearing loss. It is important to study and understand all aspects of the many different types and reasons for hearing loss. The loss of this particular sense can be socially debilitating. It can affect the communication skills of the person, not only in receiving information, but also in giving the correct response. This paper focuses primarily on hearing loss in the elderly. One thing that affects older individuals' communication is the difficulty they often experience when recognizing time compressed speech. Time compressed speech involves fast and unclear conversational speech. Many older listeners can detect the sound of the speech being spoken, but it is still unclear (Pichora-Fuller, 2000). In order to help with diagnosis and rehabilitation, we need to understand why speech is unclear even when it is audible. The answer to that question would also help in the development of hearing aids and other communication devices. Also, as we come to understand the reasoning behind this question and as we become more knowledgeable about what older adults can and cannot hear, we can better accommodate them in our day to day interactions.
As our research into science and technology ever increases its seems inevitable that in the near future Artificial Intelligent machines will exist and become part of our everyday life such as we see with modern computers today.
Flanagan, J., Research in Speech Communication (Oct. 24, 1995), Proceedings of the National Academy of Sciences of the United States of America, Vol. 92, No. 22 pp. 9938-9945
Imagine asking your computer to do something in the same way you would ask a friend to do it. Without having to memorize special commands that only it could understand. For computer scientists this has been an ambitious goal; that can further simplify computers. Artificial Intelligence, a system that can mimic human intelligence by performing task that usually only a human can do, usually has to use a form of natural language processing. Natural language processing, a sub-field of computer science and artificial intelligence, concerns the successfully interaction between a computer and a human. Currently one of the best examples of A.I.(Artificial Intelligence) is IBM 's Watson. A machine that gained popularity after appearing on the show
...speaker and the listener. The student can store often used responses, and prepare anticipated answers prior to situations where he will be meeting with those less familiar with his speech capabilities. By implementing this type of device, the student has become more confident and can communicate appropriately for a student his age. In this instance, the integration of technology into the learning environment may make a difference as to whether the student is employable or overlooked due to the inability to communicate well on the job.
A modern example would include speech recognition within cellular devices. Skype has also produced intelligence that can translate speech in record time. Other examples include self-driving cars, programs that can identify objects in videos, and robotic canines that can imitate life-like behavior from a real dog. There has been an exponential spike in the capability of computer systems and the demand for professionals who can make self-identifying and operating robotics conceivable. The boundaries between science and science fiction are being presented in front of societies’ eyes. As much as society thinks the technology is not prevalent today, the capability and prototypes are present.
This is similar to the life of any computer. Humans gain information through the senses. Computers gain similar information through a video camera, a microphone, a touch pad or screen, and it is even possible for computers to analyze scents and chemicals. Humans also gain information through books, other people, and even computers, all of which computers can access through software, interfacing, and modems. For the past year, speech recognition software products have become mainstream(Lyons,176).
Jurafsky, D. & Martin, J. H. (2009), Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed, Pearson Education Inc, Upper Saddle River, New Jersey.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.