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Special attention is given to the pharmacological treatment of combined medication of Carvedilol and hydrochlorothiazide which is the most effective and the most beneficial therapy for hypertensive patients with diabetes and various metabolic comorbidities. This work represents spectrophotometric platform scenarios based on factorized spectrum (FS) using interpoint data difference resolution scenarios (IDDRS) coupled with spectrum subtraction method (SS) for the concurrent quantification of carvedilol (CAR) and hydrochlorothiazide (HCT) when present together in a combination without the need for any initial physical separation steps. This IDD resolution scenario based on manipulating the zero-order spectra (D 0 ) of both drugs in the mixture with various spectral features at different wavelength regions (200–400 nm), region I (220–250 nm), region II (240–300 nm) and region III (270–320 nm) via absorbance resolution (AR) and induced absorbance resolution (IAR) methods coupled with corresponding spectrum subtraction (SS). The calibration curves were established across the linearity ranges of 2.0–12.0 µg/mL at 242.50 nm and 4.0–40.0 µg/mL at 285.5 nm for CAR and 1.0–11.0 µg/mL at 226.10 nm and 2.0–20.0 µg/mL at 270.5 nm for HCT. Moreover, methods’ validation was confirmed via ICH guidelines. A Multicenter comparison between sensitivity, specificity in respect resolution sequence were applied using different wavelength regions with various concentration ranges was applied and finally spectral resolution recommendation is issued and cumulative validation score (CVS) is calculated as an indicator in the risk analysis. In quality control laboratories, the studied approaches are applicable for conducting analysis on the mentioned drugs. In addition, the selection of spectrophotometry aligns with the principles of green analytical chemistry, an approach that resonates with the overarching theme of minimizing environmental impact. Via four metric tools named: analytical greenness (AGREE), green analytical procedure index (GAPI), analytical eco-scale, and national environmental method index (NEMI), methods’ greenness profile was guaranteed.

Speech data collected under uncontrolled environment need to be processed to build a robust automatic speech recognition system. In this paper, a method is proposed to process the degraded speech signal. Initially, the significance of the spectral subtraction with voice activity detection (SS-VAD) and magnitude squared spectrum estimators are studied for different types of noises. In SS-VAD method, the degraded speech data is sampled and windowed into 50% overlapping. The VAD is used to detect the voiced regions of speech signal. The minimum mean square error-short time power spectrum, minimum mean square error-spectrum power based on zero crossing (MMSE-SPZC) and maximum a posteriori estimators are studied individually. These MSS estimators are implemented on the assumption that the magnitude squared spectrum of the degraded speech signal is the sum of the clean (original) speech signal and noise model. The experimental results show that the MMSE-SPZC estimator gives better performance compared to the other two methods. This estimator is combined with SS-VAD method to improve the performance. In this paper, the combined SS-VAD and MMSE-SPZC method, yields better speech quality by reducing noise in degraded speech signal compared to the individual methods.

In this paper, the improvements in the recently implemented Kannada speech recognition system is demonstrated in detail. The Kannada automatic speech recognition (ASR) system consists of ASR models which are created by using Kaldi, IVRS call flow and weather and agricultural commodity prices information databases. The task specific speech data used in the recently developed spoken dialogue system had high level of different background noises. The different types of noises present in collected speech data had an adverse effect on the on line and off line speech recognition performances. Therefore, to improve the speech recognition accuracy in Kannada ASR system, a noise reduction algorithm is developed which is a fusion of spectral subtraction with voice activity detection (SS-VAD) and minimum mean square error spectrum power estimator based on zero crossing (MMSE-SPZC) estimator. The noise elimination algorithm is added in the system before the feature extraction part. An alternative ASR models are created using subspace Gaussian mixture models (SGMM) and deep neural network (DNN) modeling techniques. The experimental results show that, the fusion of noise elimination technique and SGMM/DNN based modeling gives a better relative improvement of 7.68% accuracy compared to the recently developed GMM-HMM based ASR system. The least word error rate (WER) acoustic models could be used in spoken dialogue system. The developed spoken query system is tested from Karnataka farmers under uncontrolled environment.