| dc.description.abstract | In this paper, we focused on automation of Dunstan Bab) Language. This system uses MFCC as feature e\lraetion and codebook as ti!oture matching. The codebook or clusters is made from the proceeds of all the buhy's cries du1a, by using the k-mcans clustering. The datn is taken from Dunstan Baby Language videos 1hat has been processed. The d:ita is divided into two, 1raining data and testing data. There are 140 training dato, each of \\hich represents the 28 hungry infant cries. 28 sleepy infant cries, 28 \\anted to burp infant cries, 28 in pain infant cric,. and 28 uncomfortable infant cries (could be because his diaper is wet/too hot/cold air or an} thing else}. lhe lc~ung data is 35, respectively 7 infant cries for each type of infant cry. The research '.tr) i ng frame length: 25 ms/frame length = 275. 40 ms/frame length = 440. 60 ms/ frame length = 660. O\ erlap frame: 0°o. 25~o. 40%, the number of codewords: I to 18, except for frame length 275 and O\.crlap frame = O u-,ing I to 29 clusters. The identification of this type of infant cries uses the minimum distance of euclidean distance. Accuraq \aluc is between 37% and 94%. Sound 'ch' is the most familiar. whereas sound ·m,11· is always missunderstood and generally it is known as 'neh' and 'eairh'. The weakness point of this research is the silent is onl) be cut at the beginning and at the end of speech signal. Hopefully. in the nc\t research. the silent can be cut in the middle of sound so that it can produce more specific sound. It has impucl on the bigger accurac) as well. | en |
