Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm.

As a result of the fast rise of globalization, people in China are learning English at a rapid pace. However, there is a severe shortage of English teachers in the region, which is a major hindrance. To address these concerns, a deep learning-based algorithm is proposed that can not only check English pronunciation but also help learners distinguish between phonemic and quality phonemic while listening and differentiating, as well as correct phonemic errors, thereby increasing their language learning capacity. In order to study the application of nonlinear network identification technology in English learning, this paper evaluates the English pronunciation quality through the deep learning algorithm of deep learning combined with the related contents of neural network data model, and the experimental results of speech recognition structure are analyzed and discussed in detail. The concordance between machine and manual intonation evaluation is 80%, the concordance rate of adjacent intonation evaluation is 98.33%, and the Pearson correlation coefficient is 0.627 that shows the technique is reliable. The method of English pronunciation and speech identification model is sensible and dependable, which can give beginners a punctual, exact and impartial judgment and response guidance, assist learners to get on the differences between their phonemic and standard phonemic, and correct phonemic mistakes, in order to enhance the ability of oral English learning.

Valorization of microalgae biomass into bioproducts promoting circular bioeconomy: a holistic approach of bioremediation and biorefinery.

The need for alternative source of fuel has demanded the cultivation of 3rd generation feedstock which includes microalgae, seaweed and cyanobacteria. These phototrophic organisms are unique in a sense that they utilise natural sources like sunlight, water and CO2 for their growth and metabolism thereby producing diverse products that can be processed to produce biofuel, biochemical, nutraceuticals, feed, biofertilizer and other value added products. But due to low biomass productivity and high harvesting cost, microalgae-based production have not received much attention. Therefore, this review provides the state of the art of the microalgae based biorefinery approach to define an economical and sustainable process. The three major segments that need to be considered for economic microalgae biorefinery is low cost nutrient source, efficient harvesting methods and production of by-products with high market value. This review has outlined the use of various wastewater as nutrient source for simultaneous biomass production and bioremediation. Further, it has highlighted the common harvesting methods used for microalgae and also described various products from both raw biomass and delipidified microalgae residues in order to establish a sustainable, economical microalgae biorefinery with a touch of circular bioeconomy. This review has also discussed various challenges to be considered followed by a techno-economic analysis of the microalgae based biorefinery model.