AI Driven Chatbot Counsellor

Speech and written information are fundamental to human communication. As a result, the majority of spoken and textual communication takes place on digital platforms like Twitter, Facebook, and WhatsApp, among others. Our model employs dual recurrent neural networks (RNNs) to encode the information from text and audio sequences, as spoken language and sound constitute emotional discourse. The emotion class is then predicted by combining the data from the two sources. Due to the complexity of speech emotion recognition, models that use audio properties to generate powerful classifiers have become increasingly important. Completing sentences or crafting sentences from a given starting word is a major aspect of natural language processing. In a way, it shows if a machine is capable of human creativity and mental processes. In order to assist handle diverse phrase generation challenges, we use natural language processing to train the machine for certain tasks. This is especially useful for application situations such as machine translation, automatic question answering, and summary creation. At the moment, OpenAI GPT and BERT are the most widely used language models for text generation and prediction. The approaches based on handwritten instructions, patterns, or statistical methods have been quickly superseded by the latest developments in deep learning and artificial intelligence, such as end-to-end trainable neural networks. This research presents a novel approach to deep neural learning-based chatbot creation. This approach builds a multilayer neural network to analyse and learn from the data. Furthermore, we employ supplementary limitations on the generation model to generate the correct response, which is capable of discerning the context of the discussion, the user’s mood, and the anticipated response. This enables us to provide customised counselling replies depending on customer feedback. Through this study, two new corpora will be used to train the OpenAI GPT model, which will then be used to generate articles and long sentences. Finally, a comparison study will be carried out. Concurrently, we will use the BERT model to complete the task of context-based intermediate word prediction.