This paper focuses on marker based watershed segmentation algorithms. As marker based watershed segmentation algorithm causes over segmentation and cause noise in the image produced. So to reduce these problem different researchers has proposed different solutions, but the best solution is to use bilateral filter. The main objective of this paper is to find the gaps in existing literature. The different segmentation techniques are reviewed and found that marker based is best in most of cases because it marks the regions then segment them. But optimizing the marking regions is still an area of research.
Speech is an ancient field of study and research is being done on it till date. Automatic Speech recognition system deals with analysis and recognition of the input speech signal by the machine or computer in various environments. To enhance the accuracy and capability of the system various feature extraction techniques are implemented. This research paper provides a brief overview of Speech recognition system and its various phases like analysis, feature extraction, modeling and testing or matching. In addition it also includes detailed and comparative study on Linear Predictive Coding (LPC) feature extraction techniques used in Automatic Speech Recognition systems. The main objective of this paper is to briefly summarize speech recognition system and three feature extraction methods that are an integral part of ASR.
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