Informatics Publishing Limited

Monica Kachroo , S. S. Agrawal , Alok Arya

Abstract

The ethanolic extract of the seeds of Ensete superbum Cheesm (Family Musaceae) furnished a compound with structure established as 4-(4-hydroxy-3-methyl-hex-5-enyl)-chroman-2, 7-diol on the basis of physical and spectral basis. The anti-implantation activity of the ethanolic extract at doses of 100, 125 and 250 mg/kg body weight when administered on days 1-7 were 86.9, 94.4, and 98.8% respectively, as compared to the isolated fraction at doses of 2.5 and 5 mg/kg body weight was 87.5 and 96.0% respectively as compared to their Control group. The isolated fraction was found to exhibit significant anti-estrogenic activity in immature female rats at the dose of 5 mg/kg, and the ethanolic extract exhibited very significant activity at the dose of 250 mg/kg-body weight. Histopathological studies revealed significant anti-ovulatory activity in immature female rats at the dose 5 mg/kg. The oral LD₅₀ value of the ethanolic extract of Ensete superbum Cheesm was found to be 3235.9 mg/kg.

Keywords

Ensete Superbum Cheesm, NMR, Chroman-2, 7-diol, Anti-implantation, Anti-estrogenic And Anti-ovulatory Activity

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Shweta Bansal ¹, Atul Kumar ², S. S. Agrawal ¹

Affiliations
1 KIIT College of Engineering, Gurgaon - 122102, Haryana, IN
2 Ansal University, Gurgaon - 122003, Haryana, IN

Abstract

This work compares the performance of the Mel-Frequency Cepstral Coefficient (MFCC) and Perceptual Linear Prediction (PLP) features for developing a text-dependent speaker identification system. Continuously spoken Hindi speech sentences have been used to train the HMM models using HTK toolkit for each speaker separately. The experiments have been performed using a set of 200 continuously spoken sentences with vocabulary of 20000 isolated words using a database of 100 speakers. The results show an accuracy of 92.26% recognition when PLP features have been used and accuracy of 91.18% for MFCC features. A confusion matrix has been created for all the 20 test speakers based on the recognition scores obtained for each of these speakers and their confusion with other speakers. Performance has been compared in the closed set and open set conditions of testing and as it is expected, the performance in the closed set condition is far better than the open set. We propose that if PLP features are used in place of MFCC, they may provide improvement in speaker identification accuracy by reducing the cases of false acceptance.

Keywords

Hindi Speech, HMM, MFCC, RASTA-PLP, Speaker Identification.

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