Voice-Based Smart Assistant System for Vehicles using RASA

Conversational AIs, or chatbots, mimic human speech when conversing. Smart assistants facilitate the automation of several tasks that needed human intervention earlier. Because of their accuracy, absence of dependence on human resources, and accessibility around the clock, chatbots can be employed in vehicles too. Due to people's propensity to divert their attention away from the task of driving while engaging in other activities like calling, playing music, navigation, and getting updates on the weather forecast and latest news, road safety has declined and accidents have increased as a result. It would be advantageous to automate these tasks using voice commands rather than carrying them out manually. This paper focuses on the development of a voice-based smart assistance application for vehicles based on the RASA framework. The smart assistant provides functionalities like navigation, communication via calls, getting weather forecasts and the latest news updates, and music that are completely voice-based in nature.

Experimental Evaluation of Deep Learning models for Marathi Text Classification

The Marathi language is one of the prominent languages used in India. It is predominantly spoken by the people of Maharashtra. Over the past decade, the usage of language on online platforms has tremendously increased. However, research on Natural Language Processing (NLP) approaches for Marathi text has not received much attention. Marathi is a morphologically rich language and uses a variant of the Devanagari script in the written form. This works aims to provide a comprehensive overview of available resources and models for Marathi text classification. We evaluate CNN, LSTM, ULMFiT, and BERT based models on two publicly available Marathi text classification datasets and present a comparative analysis. The pre-trained Marathi fast text word embeddings by Facebook and IndicNLP are used in conjunction with word-based models. We show that basic single layer models based on CNN and LSTM coupled with FastText embeddings perform on par with the BERT based models on the available datasets. We hope our paper aids focused research and experiments in the area of Marathi NLP.