A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Akila, A.
- Investigation on Oracle Data Miner Purpose-Directed & Undirected
Authors
1 Department of Computer Science & Engineering, AVC College of Engineering, Mayiladuthurai, IN
Source
Data Mining and Knowledge Engineering, Vol 6, No 7 (2014), Pagination: 307-310Abstract
The majority of businesses have a vast quantity of information, with an immense pact of information extract in it, "extracting" is typically what it is doing: a great deal of information survive that it engulfs a usual way of information scrutiny. Data extracting affords a means to obtain the information hidden in the information. Data extracting crafts replicas to unearth secret molds in hefty, intricate anthology of information, molds that occasionally evade usual statistical looms to scrutiny because of the hefty amount of aspects, the intricacy of molds or the intricacy in executing the scrutiny. In this paper we will discuss the data extraction in oracle database, oracle data extraction and the algorithm used in the oracle data extraction. The functions of oracle data extraction like directed and undirected sets will be explained using different algorithms.Keywords
Data Extract, Oracle Database, Directed, Undirected.- Word Based Tamil Speech Recognition Using Temporal Feature Based Segmentation
Authors
1 D.J. Academy for Managerial Excellence, IN
2 Department of Computer Science, Bharathiar University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 5, No 4 (2015), Pagination: 1037-1043Abstract
Speech recognition system requires segmentation of speech waveform into fundamental acoustic units. Segmentation is a process of decomposing the speech signal into smaller units. Speech segmentation could be done using wavelet, fuzzy methods, Artificial Neural Networks and Hidden Markov Model. Speech segmentation is a process of breaking continuous stream of sound into some basic units like words, phonemes or syllable that could be recognized. Segmentation could be used to distinguish different types of audio signals from large amount of audio data, often referred as audio classification. The speech segmentation can be divided into two categories based on whether the algorithm uses previous knowledge of data to process the speech. The categories are blind segmentation and aided segmentation.
The major issues with the connected speech recognition algorithms were the vocabulary size will be larger with variation in the combination of words in the connected speech and the complexity of the algorithm is more to find the best match for the given test pattern. To overcome these issues, the connected speech has to be segmented into words using the attributes of speech. A methodology using the temporal feature Short Term Energy was proposed and compared with an existing algorithm called Dynamic Thresholding segmentation algorithm which uses spectrogram image of the connected speech for segmentation.