Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

Speaker verification is a biometric-based method for individual authentication. However, there are still several challenging problems in achieving high performance in short utterance text-independent conditions, maybe for weak speaker-specific features. Recently, deep learning algorithms have been used extensively in speech processing. This manuscript uses a deep belief network (DBN) as a deep generative method for feature extraction in speaker verification systems. This study aims to show the impact of using the proposed method in various challenging issues, including short utterances, text independence, language variation, and large-scale speaker verification. The proposed DBN uses MFCC as input and tries to extract more efficient features. This new representation of speaker information is evaluated in two popular speaker verification systems: GMM-UBM and i-vector-PLDA methods. The results show that, for the i-vector-PLDA system, the proposed feature decreases the EER considerably from 15.24 to 10.97%. In another experiment, DBN is used to reduce feature dimension and achieves significant results in decreasing computational time and increasing system response speed. In a case study, all the evaluations are performed for 1270 speakers of the NIST SRE2008 dataset. We show deep belief networks can be used in state-of-the-art acoustic modeling methods and more challenging datasets.

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Ultra-broadband, wide-angle plus-shape slotted metamaterial solar absorber design with absorption forecasting using machine learning

Patel

Parmar

Katkar

2022

Sci Rep

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Energy utilization is increasing day by day and there is a need for highly efficient renewable energy sources. Solar absorbers with high efficiency can be used to meet these growing energy demands by transforming solar energy into thermal energy. Solar absorber design with highly efficient and Ultra-broadband response covering visible, ultraviolet, and near-infrared spectrum is proposed in this paper. The absorption response is observed for three metamaterial designs (plus-shape slotted design, plus-shape design, and square-shape design) and one optimized design is used for solar absorber design based on its high efficiency. The design results are compared with AM 1.5 spectral irradiance response. The electric field response of the plus-shape slotted metamaterial design is also presented which matches well with the absorption results of different solar spectrum regions. The results proved that the attained absorption response showing wide angle of incidence. Machine learning is also used to examine the design data in order to forecast absorption for various substrate thickness, metasurface thickness, and incidence angles. Regression and forecasting simulations based on machine learning are used to try to anticipate absorber behaviour at forthcoming and intermediate wavelengths. Simulation results prove that Machine Learning based methods can lessen the obligatory simulation resources, time and can be used as an effective tool while designing the absorber. The proposed highly efficient, wide-angle, ultra-broadband solar absorber design with its behavior prediction capability using machine learning can be utilized for solar thermal energy harvesting applications.

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Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

Cited by 8 publications

References 65 publications

Statistically Optimal Joint Multimicrophone MAP Estimators Under Super-Gaussian Assumption

Statistically Optimal Joint Multimicrophone MAP Estimators Under Super-Gaussian Assumption

Analysis of Deep Generative Model Impact on Feature Extraction and Dimension Reduction for Short Utterance Text-Independent Speaker Verification

Ultra-broadband, wide-angle plus-shape slotted metamaterial solar absorber design with absorption forecasting using machine learning

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