Chapter · July 012 citation reads 9,926 author

2.2 | LITERATURE REVIEW 
 
2.2.1 | Pattern Recognition 
 
Pattern recognition, one of the branches of artificial intelligence, sub-section 
of machine learning, is the study of how machines can observe the environment, 
learn to distinguish patterns of interest from their background, and make sound and 
reasonable decisions about the categories of the patterns. A pattern can be a 
fingerprint image, a handwritten cursive word, a human face, or a speech signal, 
sales pattern etc… 
The applications of pattern recognition include data mining, document 
classification, financial forecasting, organization and retrieval of multimedia 
databases, and biometrics (personal identification based on various physical 
attributes such as face, retina, speech, ear and fingerprints).The essential steps of 

---

Chapter 2 | Speech Recognition
8
pattern recognition are: Data Acquisition, Preprocessing, Feature Extraction, 
Training and Classification. 
Features are used to denote the descriptor. Features must be selected so that 
they are discriminative and invariant. They can be represented as a vector, matrix, 
tree, graph, or string.
They are ideally similar for objects in the same class and very different for 
objects indifferent class. Pattern class is a family of patterns that share some 
common properties. Pattern recognition by machine involves techniques for 
assigning patterns to their respective classes automatically and with as little human 
intervention as possible.
Learning and Classification usually use one of the following approaches: 
Statistical Pattern Recognition is based on statistical characterizations of patterns, 
assuming that the patterns are generated by a probabilistic system. Syntactical (or 
Structural) Pattern Recognition is based on the structural interrelationships of 
features. Given a pattern, its recognition/classification may consist of one of the 
following two tasks according to the type of learning procedure: 
1) Supervised Classification (e.g., Discriminant Analysis) in which the input pattern 
is identified as a member of a predefined class. 
2) Unsupervised Classification (e.g., clustering) in which the pattern is assigned to 
a previously unknown class.