Elevating intelligent voice assistant chatbots with natural language processing, and OpenAI technologies

• DOI: 10.11591/ijeecs.v37.i1.pp507-517
• Publication Date: 2024-10-31T16:28:29Z
• Authors: Nilesh B. Korade; Mahendra B. Salunke; Amol A. Bhosle; Gayatri G. Asalkar; Bechoo Lal; Prashant B. Kumbh...
• Keywords: Chatbot

Businesses can offer support to customers outside of usual business hours and across time zones by employing chatbots, which provide round-the-clock support. Chatbots react user inquiries quickly, reducing wait times improving customer satisfaction. It becomes challenging for the chatbot differentiate between two queries that users pose carry same meaning, making it harder understand appropriately. The aim this research is develop a capable understanding semantic meaning questions as well recognizing various speech patterns, accents, dialects accurate responses. In research, we have implemented voice-enabled system where verbally questions, provides responses through voice assistance. architecture incorporates several key components: question-answer database, OpenAI embedding representation, text-to-speech (TTS) speech-to-text (STT) audio-to-text text-to-audio conversion, respectively. Specifically, utilized encode into vector representations, enabling efficient similarity calculations. Additionally, extreme gradient boosting (XGBoost) trained on embeddings identify similarities within dataset. This framework allows seamless interaction chatbot, leveraging state-of-the-art technologies in natural language processing (NLP) recognition. outcome demonstrates XGBoost model delivers excellent outcomes when tuned with particle swarm optimizer (PSO). OpenAI-generated has good potential capturing sentence information models it.