An Emotionally intelligent-Ai voice chatbot

An Emotionally Intelligent Voice Chatbot Using Deep Learning with Retrieval-Augmented Generation and Few-Shot Voice Cloning for Personalized Human-Computer Interaction

This project proposes the development of an advanced voice chatbot capable of emotionally intelligent conversations, powered by Retrieval-Augmented Generation (RAG) and few-shot voice cloning. The chatbot leverages state-of-the-art deep learning techniques to understand user intention, detect emotional tone, and able to generate content for user.

Abstract :

This project proposes the development of an advanced voice chatbot capable of emotionally intelligent conversations, powered by Retrieval-Augmented Generation (RAG) and few-shot voice cloning. The chatbot leverages state-of-the-art deep learning techniques to understand user intention, detect emotional tone, and generate contextually relevant and emotionally appropriate responses, Additionally, the system incorporates a voice cloning module that can replicate a specific person’s voice using minimal training data, enabling personalized interactions in the desired voice.The chatbot will not only understand and respond to user queries effectively but also adapt its tone and voice to match the emotional context of the interaction, all while speaking in a cloned voice generated from minimal audio samples.These technologies able to deliver a highly personalized and emotionally intelligent conversational experience The architecture integrates speech-to-text (STT) for input processing, RAG for response generation, emotion-aware response modification, and text-to-speech (TTS) with cloned voice synthesis. This project aims to revolutionize customer service and human-computer interaction by providing a highly personalized and emotionally resonant conversational experience. The proposed system has applications in customer support, virtual assistants, and therapeutic chatbots, offering a new dimension of empathy and personalization in AI-driven communication.

Project Overview

The goal of this project is to create a voice chatbot that:

1. Understands and responds to user queries using Retrieval-Augmented Generation (RAG).

2. Detects and responds to emotions in user input.

3. Clones a specific person’s voice using minimal audio samples.

4. Interacts with users in a personalized and emotionally impactful interactions with users.

Deep Learning Architecture

The chatbot architecture consists of the following components:

1. Retrieval-Augmented Generation (RAG)

• Purpose: Generate context-aware responses.

• Model: Use pre-trained RAG models (RAG-Token or RAG-Sequence) from Hugging Face Transformers.

• Fine-tuning: Fine-tune RAG on domain-specific conversational datasets.

2. Emotion Detection

• Purpose: Detect emotional tone in user input.

• Model: Use a transformer-based model (e.g., Emotion RoBERTa) trained on emotional datasets like CREMA-D or RAVDESS.

• Integration: Modify RAG responses based on detected emotions.

3. Voice Cloning

• Purpose: Clone a specific person’s voice.

• Model: Use few-shot voice cloning models like SV2TTS (Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis).

• Training: Train on 5-10 minutes of target speaker audio.

4. Text-to-Speech (TTS)

• Purpose: Convert text responses into speech.

• Model: Use neural TTS systems like Tacotron 2 or WaveNet.

• Integration: Fine-tune TTS to match the cloned voice.

5. Speech-to-Text (STT)

• Purpose: Convert user speech into text.

• Model: Use pre-trained models like OpenAI Whisper or DeepSpeech.

6. Integration Pipeline

1. User speech → STT → Text input.

2. Text input → Emotion detection → Emotional context.

3. Emotional context + Text input → RAG → Context-aware response.

4. Response → TTS with cloned voice → Audio output.

Conclusion:

This project combines cutting-edge technologies in Deep learning & NLP, speech processing, and emotional AI to create a voice chatbot that is both emotionally intelligent and capable of personalized voice interactions. By leveraging RAG, emotion detection, and voice cloning.

This project revolutionizes content creation across industries,the chatbot offers a unique and innovative solution for applications in customer service, virtual assistants, and therapeutic chatbots. For podcasters, it streamlines production by cloning your voice, allowing quick application to episodes and saving hours of recording time. Educators benefit by generating courseware narrations effortlessly using text-to-speech, reducing the need for repetitive recordings. Filmmakers can replicate voices with lifelike quality, maintaining consistent tone and mood for seamless sound effects and dubbing. Marketers can create natural voiceovers for videos and presentations, ensuring professional and engaging content tailored to their brand. This technology enhances efficiency, creativity, and personalization in content creation.