1. Artificial Intelligence for Speech Recognition
Topic:-
Artificial Intelligence for Speech Recognition

2. Artificial Intelligence (or AI ) : -
Definition:- The study and design of intelligent agents & also used to describe a property of machines or programs
Among researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate.
Applications of AI -
Pattern Recognition
Hand Recognition
Speech Recognition
Natural Language Processing
Face Recognition
Artificial Creativity
Non linear controls and Robotics

3. Speech Recognition-
Speech recognition converts spoken words to machine-readable input. It is also called Voice Recognition.
Speech recognition includes-
voice dialing
content-based spoken audio search
speech-to-text processing
Audio visual Speech Recognition is also present in which it takes lip reading also apart from speech recognition.

4. Speech Recognition in Cellphones-
Callers words are captured and digitized by speech-recognition system.
Digitized voice is split into individual frequency components, called spectral representations.
The components are translated into phonemes.
Complex models and algorithms determine a likely translation.

5. Performance of speech recognition systems-
It is usually specified in terms of accuracy and speed. Accuracy may be measured in terms of performance accuracy which is usually rated with word error rate , whereas speed is measured with the real time factor.
Dictation machines can achieve very high performance in controlled conditions and require only a short period of training.
Optimal conditions usually assume that users -
have speech characteristics which match the training data.
can achieve proper speaker adaption.
work in clean and no noise environment.
There are 2 models on statistically- based Speech Recognition-
Hidden Markov Model (HMM model)
Dynamic Time Wrapping (DTW model)

6. 1. HMM - based Speech Recognition -
These are statistical models which output a sequence of symbols or quantities.
Two reasons why HMMs are mainly used and popular-
Speech signal could be veiwed as a piecewise stationary signal.
They can be trained automatically , simple and computationally feasible to use
The Hidden Markov Model would output sequence of n-dimensional real-valued vectors ,outputting one of these every 10 milliseconds. The vectors would consist of cepstral coefficients.

7. 2. DTW - based Speech Recognition -
Dynamic time warping is an algorithm for measuring similarity between two sequences which may vary in time or speed. It is a historical approach.
Similarities between speaking patterns would be detected. DTW has been applied to video, audio, and graphics -- indeed, any data which can be turned into a linear representation can be analyzed with DTW.
This sequence technique is also used in HMMs model.

8. Applications of Speech Recognition -
Health Care -
In this even in the wake of Speech recognition technologies MT haven’t become obsolute.
Military -
High-performance fighter aircraft-
Speech recognizers have been operated successfully.
Some important conclusions from the work are as follows:
1. Speech recognition has definite potential for reducing pilot workload, but this potential was not realized consistently.
2. Achievement of very high recognition accuracy (95% or more) was the most critical factor for making the speech recognition system useful - with lower recognition rates, pilots would not use the system.
3. More natural vocabulary and grammar, and shorter training times would be useful, but only if very high recognition rates could be maintained.

9. Helicopters - As in fighter applications overriding issue for voice in helicopters is the impact on pilot effectiveness.
Battle Management – Speech recognition equipment was tested in conjunction with an integrated information display for naval battle management applications.
Telephony and other domains – ASR in the field of computer gaming and simulation is becoming more widespread.
Disabled people – These people are another part of population that benefit from speech recognition programs.

10. Failures of Speech Recognition-
The computer has trouble with "sound-alike" errors.  It's hard to get mad at the computer for not recognizing mumbling.  But it can be frustrating when you think you are speaking clearly, and it just isn't good enough.   
For example, when I said: I sure look forward to seeing you
The computer heard: Assure look forward to seen in you
When I repeated the same words with better enunciation, the computer got it right.
Using Laptops
On the Roads

11. Supporting Speech Recognition-
To be successful, most firms should -
Set up a pilot group of patient lawyers to try it out .
Have the "techies" use the PC directly.   Only use software that lets the secretary or proofreader listen to what was dictated. 
Have a pilot group of lawyers who already use dictating machines enter text using a dictating machine.
When you roll the system out to the firm, be ready with personal trainers and floor support. 
Require everything to be proofread carefully. 

12. Conclusion –
This paper presents the Speech Recognition in Artificial intelligence systems and it is important to consider the environment in which the speech recognition system has to work.
The grammar used by the speaker and accepted by the system, noise level, noise type, position of the microphone, and speed and manner of the user’s speech are some factors that may affect the quality of speech recognition

13. Thank you

14. Queries