Survey on AI driven Chatbot Counselor

When it comes to human communication, speech and written data are essential. Therefore, textual and spoken contact between people occurs mostly through digital programs like Facebook, WhatsApp, and Twitter, among others. Since spoken language and sound make up emotional conversation, our model uses dual recurrent neural networks (RNNs) to encode the information from text and audio sequences. It then integrates the information from both sources to predict the emotion class. The difficult issue of speech emotion identification has led to a great deal of dependence on models that leverage audio characteristics to create effective classifiers. One significant area of natural language processing is filling in sentences or creating sentences from a given starting word. It illustrates if a computer is capable of human creativity and thought processes in one sense. We employ natural language processing to train the machine for certain tasks and then use it to help address various phrase production difficulties, particularly for application scenarios like summary creation, machine translation, and automatic question answering. Currently, the most popular language models for text creation and prediction are OpenAI GPT and BERT. The approaches based on handwritten instructions, patterns, or statistical methods have been quickly superseded by the latest developments in deep learning and artificial intelligence, such as end-to-end trainable neural networks.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. Additionally, we utilise additional constraints to the generation model for the right answer generation, which can identify the conversational context, the user’s emotion, and the expected reply. This allows us to develop individualised counselling responses based on user input. This work will train the OpenAI GPT model on two new corpora, which will then be utilised to produce articles and lengthy sentences. Lastly, a comparative analysis will be conducted. Simultaneously, we will finish the job of context-based intermediate word prediction using the BERT model.