Artificial Intelligence for Speech Recognition BE Seminar

The research paper Artificial Intelligence for Speech Recognition BE Seminar speaks of Speak Recognition as a domain within Artificial Intelligence. The paper talks about the study and design of intelligent agents & also used to describe a property of machines or programs. Among researchers hope machines will exhibit the faculties of 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

 Speech recognition converts spoken words to machine-readable input. It is also called Voice Recognition. The paper deals withvarious aspects of Speech recognition. Speech recognition includes-

  •  Voice dialing
  •  Content-based spoken audio search
  •  Speech-to-text processing
  • Performance of speech recognition systems

 The paper states that speech recognition 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. The paper suggests and explains different types of speech recognition models.

HMM – based Speech Recognition: These are statistical models which output a sequence of symbols or quantities.

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.

 Applications of Speech Recognition: The application of Speech Recognition is diversified and penetrates into the fields of HealthCare and Military.

The paper also talks about Speech recognizers that have been operated successfully.

The paper concludes by quoting some effective findings.

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.

Drawbacks: 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 one thinks one is speaking clearly, and it just isn’t good enough.


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.

Download Artificial Intelligence for Speech Recognition BE Seminar.

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