MFCC Based Technique for Speech Pattern Recognition Using Soft Computing

V Yadaiah, R P Dr, Singh
2017 International Journal of Innovations & Advancement in Computer Science IJIACS   unpublished
Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the
more » ... . By linking the ideas and techniques of soft computing with other disciplines, one can serve as a unifying platform that fosters comparisons, extensions, and new applications. This paper discussed about MFCC based speech pattern recognition. KEY WORDS: Soft computing, pattern recognition, fuzzy logic,MFCC. INTRODUCTION There are many traditional methods for pattern recognition which are used intensively. These days, the methodology of artificial neural is very popular. There are many things which needed to be cared in order to design a recognition system. These points are: definition of pattern classes, environment of sensors, representation of patterns, extraction of features, analysis of selection, test samples and their training and finally, the evaluation of performance. In spite of a lot of research work done, the significant problem of complex patterns and their recognition with accurate location and scale have been unsolved. The pattern recognition techniques are widely used in many other applications such as data mining, face recognition, handwriting recognition etc. and in much more applications. A pattern can be anything. It can be human face, signals of speech, handwritten word etc. The problem of pattern recognition is classifies as classes which are defined by the system designer. The system designer has the full right to specify the needed constraints on the classes. With the advancement of technology, research works are going on inventing new techniques to make the process of data analysis less complex. Since, most of the companies have large databases, so the need of an automatic pattern recognition system is there and engineers are working in that direction. There are primarily three aspects to design a pattern recognition system. These are: pre-processing and acquisition of data, representation of data and decision making. These three components are essential for designing a pattern recognition system. Artificial neural networks are also used for the purpose of pattern recognition. The reason behind popularity of these networks is their capability to learn complex relationships easily and procedural algorithm used by these networks. Feed-forward network is the best type of neural networks which is used quite regularly for pattern recognition. The reason behind the most usage of this feed-forward network is the presence of multi-layer perceptron in it. The architecture of whole network is updated in order to track it by using artificial neural networks. The biggest advantage of using artificial neural networks is that they don't depend much on the domain-specific knowledge and efficient algorithms used in it for the task of pattern recognition. A number of special languages have been proposed for the description of patterns such as English and Chinese characters, chromosome images, spark chamber pictures, two-dimensional mathematics, chemical structures, spoken words, and fingerprint patterns. For the purpose of effectively describing high dimensional patterns,
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