• Phonetics and Speech Sciences
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• v.5 no.4
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• pp.201-207
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• 2013

This study is a follow-up study of Han and Kang (2013) and Kang and Han (2013) which examined cross-generational changes in the Korean vowels /o/ and /u/ using acoustic analyses of the vowel formants of these two vowels, their Euclidean distances and the overlap fraction values generated in SOAM 2D (Wassink, 2006). Their results showed an on-going approximation of /o/ and /u/, more evident in female speakers and non-initial vowels. However, these studies employed non-words in a frame sentence. To see the extent to which these two vowels are merged in real words in spontaneous speech, we conducted an acoustic analysis of the formants of /o/ and /u/ produced by two age groups of female speakers while reading a letter sample. The results demonstrate that 1) the younger speakers employed mostly F2 but not F1 differences in the production of /o/ and /u/; 2) the Euclidean distance of these two vowels was shorter in non-initial than initial position, but there was no difference in Euclidean distance between the two age groups (20's vs. 40-50's); 3) overall, /o/ and /u/ were more overlapped in non-initial than initial position, but in non-initial position, younger speakers showed more congested distribution of the vowels than in older speakers.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.521-526
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• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.