BROAD PHONEME CLASSIFICATION USING SIGNAL BASED FEATURES

ABSTRACT Speech is the most efficient and popular means of human communicatio

Speaking rate attention-based duration prediction for speed control TTS

With the advent of high-quality speech synthesis, there is a lot of interest in controlling various prosodic attributes of speech. Speaking rate is an essential attribute towards modelling the expressivity of speech. In this work, we propose a novel approach to control the speaking rate for non-autoregressive TTS. We achieve this by conditioning the speaking rate inside the duration predictor, allowing implicit speaking rate control. We show the benefits of this approach by synthesising audio at various speaking rate factors and measuring the quality of speaking rate-controlled synthesised speech. Further, we study the effect of the speaking rate distribution of the training data towards effective rate control. Finally, we fine-tune a baseline pretrained TTS model to obtain speaking rate control TTS. We provide various analyses to showcase the benefits of using this proposed approach, along with objective as well as subjective metrics. We find that the proposed methods have higher subjective scores and lower speaker rate errors across many speaking rate factors over the baseline.Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Survey Analysis of Solar Power Generation Forecasting

Solar power is the conversion of sunlight into electricity using solar photovoltaic cells as a source of energy. There are various applications for solar power; here is information on PV cell generation. We seek to understand the behavior of solar power plants through the data generated by the photovoltaic modules and the power generation in different weather conditions in India. The goal of this survey is to give a thorough assessment and study of machine learning, deep learning and artificial intelligence. Artificial intelligence (AI) models as well as information preprocessing techniques, parameter selection algorithms and predictive performance evaluations are used in machine learning and deep learning models for predicting renewable energies. But in case of time series data we can predict only the errors using a linear regression model, we can also calculate things like root mean square error (RMSE), mean absolute error (MSE), mean bias error (MBE) and mean absolute percentage error (MAPE). By the analysis of weather condition also we can predict the consumption of current by solar for every 15 minutes, 1day, and 1week or even for 1 month and find the accuracy

Analysis, Monitoring and Control of Speed Variations for Various Applications of SRM Motor with ESP32 and Thing speak

Due to the growing demand for and growth in the electric vehicle (EV) market in a future civilization with advanced technology. Electric vehicles outperform traditional ones in terms of performance. Because they are environmentally friendly, EVs can help create a green and sustainable environment. They provide convenience with eco-friendliness that conventional cars cannot. Over the past 20 years, the switching reluctance motor (SRM) drive has been created and researched as a revolutionary electrical drive. The brushless switching reluctance motor drive has developed to the point that it may be used in the industrial sector as a reliable, durable, and cost- effective brushless drive with a wide speed range. In this paper, artificial intelligent controllers like Fuzzy Logic Controllers (FLC), and PID are discussed where SRM speed fluctuations are monitored and controlled by a controller using simulation analysis. Experimental analysis is carried out with prototype model. Here various speed variations are controlled and monitored using the ESP32 and Thing Speak cloud platform

Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy

A rare case of heme oxygenase deficiency: A case report and literature review

Key Clinical Message Heme oxygenase deficiency, a rare condition disrupting heme metabolism, has only nine reported cases. We present a 3‐year‐old boy with dysmorphic facies, asplenia, and normal bilirubin levels despite ongoing hemolysis. Blood transfusions sustained hemoglobin while IV steroids managed inflammation

Effectiveness of statins in people living with HIV: a systematic review and meta-analysis of randomized controlled trials

People living with HIV (PLWH) receiving statin therapy have shown improved lipid profiles. However, they are not free from side effects, thereby requiring strict monitoring of the therapy. The meta-analysis aims to analyze the effect of statins in PLWH and critically appraise the effectiveness of statin therapy in PLWH. PubMed, Scopus, and Web of Science servers were used to conduct a systematic search in compliance with the PRISMA guidelines. The meta-analysis of pooled effect estimates is produced using Revman software. A total of 12 RCTs with 8716 participants were included in the analysis. Analysis of the overall effect estimates found that statins resulted in a mean reduction of 41.15 mg/dl (MD = −41.15; 95% CI: −44.19, −38.11; p p p p = 0.0002). When considered collectively, statin therapy’s advantages appear to exceed its occasional predictable side effects like liver or muscle toxicity. PROSPERO registration ID: CRD42023469521.</p