Hybrid machine learning classification scheme for speaker identification.

Motivated by the requirement to prepare for the next generation of "Automatic Spokesperson Recognition" (ASR) system, this paper applied the fused spectral features with hybrid machine learning (ML) strategy to the speech communication field. This strategy involved the combined spectral features such as mel-frequency cepstral coefficients (MFCCs), spectral kurtosis, spectral skewness, normalized pitch frequency (NPF), and formants. The characterization of suggested classification method could possibly serve in advanced speaker identification scenarios. Special attention was given to hybrid ML scheme capable of finding unknown speakers equipped with speaker id-detecting classifier technique, known as "Random Forest-Support Vector Machine" (RF-SVM). The extracted speaker precise spectral attributes are applied to the hybrid RF-SVM classifier to identify/verify the particular speaker. This work aims to construct an ensemble decision tree on a bounded area with minimal misclassification error using a hybrid ensemble RF-SVM strategy. A series of standard, real-time speaker databases, and noise conditions are functionally tested to validate its performance with other state-of-the-art mechanisms. The proposed fusion method succeeds in the speaker identification task with a high identification rate (97% avg) and lower equal error rate (EER) (<2%), compared with the individual schemes for the recorded experimental dataset. The robustness of the classifier is validated using the standard ELSDSR, TIMIT, and NIST audio datasets. Experiments on ELSDSR, TIMIT, and NIST datasets show that the hybrid classifier produces 98%, 99%, and 94% accuracy, and EERs were 2%, 1%, and 2% respectively. The findings are then compared with well-known other speaker recognition schemes and found to be superior.

Antidiabetic potential of Andrographis echioides Nees. leaf extract on high fat diet-fed C57BL/6J diabetic mice.

The present study validates the antidiabetic potential of Andrographis echioides leaf extract (AeLE) on high fat diet-fed diabetic C57BL/6J mice. The male C57BL/6J mouse (age 6-8 weeks) were divided into 2 groups (lean control group and diabetic group). The lean control group (6 animals) was fed with standard diet pellets. The diabetic group animals (24 animals) were made diabetic by feeding a high-fat diet for 12 weeks. This group was then further divided into 4 groups of 6 animals each and treated orally (for 28 days) with vehicle (0.5%carboxymethyl cellulose), metformin 100mg/kg body weight and 2 different concentrations of test drug viz., 100mg/kg and 200mg/kg body weight. The results show a significant reduction in blood glucose and other biochemical parameters. After 28 days, the metformin and AeLE (200 mg/kg b.w) treated animals had an average serum glucose value of 129.69±1.97 mg/dl and 109.6±3.92 mg/dl, respectively. Also, the liver markers were positively affected by AeLE. In conclusion, A. echioides leaf extract was found to reduce hyperglycemia and significantly improve the biochemical profile of the mice.