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Mini project(19AI701) – Final Review. ChatBot: A Conversational AI with Natural Voice Response Submitted by: Meiyarasi V(212221230058) Deepika J (212221230016) Parshwanath M (212221230073) 2020-2024 Batch TEAM NO: 13 Under the guidance of: Mrs. Archana S H Asst.Professor Department of AIDS.

Agenda. 1. INTRODUCTION 2. PROBLEM STATEMENT 3. LITERATURE REVIEW SUMMARY 4. METHODOLOGY/FLOW 5. ALGORITHMS USED 6. IMPLEMENTATION 7. OUTPUT 8. CONCLUSION 9. REFERENCES.

Introduction. Transforming text into a voice clone involves the use of advanced technology to convert written text into spoken words that closely mimic a specific human voice. This process typically employs techniques from the field of speech synthesis, leveraging ­-artificial intelligence and deep learning models. Voice cloning has gained popularity for various applications, including virtual assistants, voiceovers, and personalized communication. At its core, the technology behind text-to-voice cloning analyzes the nuances, intonations, and unique characteristics of a selected voice to recreate it in the generated speech. This involves training models on large datasets of the chosen speaker's voice, allowing the system to learn and replicate the subtleties of pronunciation, pacing, and emotional inflections. The result is a synthetic voice that can convincingly imitate the original speaker, offering a highly realistic and natural-sounding output..

Problem Statement. Develop a text-to-voice clone that can engage in interactive dialogues, responding dynamically to user inputs with appropriate intonation and conveying a sense of conversational flow. The project aim to the advancement of assistive technology but also holds the potential to significantly impact industries such as entertainment, education, and communication..

Summary of LR. S.No. Research Technique Features Used Domain Disadvantage / Advantage Future scope 1. Language Models are Few-Shot Learners by Tom B. Brown et al. (2020) Language model meta-learning autoregressive language model Chat bot creations ADVANTAGES: The scalability of model size demonstrates improved in-context learning abilities, potentially addressing the limitations observed in smaller models DISADVANTAGES: Few-shot learning results, while promising, still lag behind state-of-the-art fine-tuned models. we invent some additional tasks designed especially to probe in-context learning abilities – these tasks focus on on-the-fly reasoning, adaptation skills, or open- ended text synthesis.

Summary of LR.

Summary of LR. 2. AI Chatbots Anupama Vijayakumar (07,July- 2020) (Artificial Intelligence Markup Language) as a key technology forcreating chatbots the advanced technologies like machine learning and artificial intelligence for Contextual Chat bots. Chat bot creations ADVANTAGES: The lightweight and configurable nature of AIML-based chatbots is also acknowledged. DISADVANTAGES: The paper mentions the limitations of menu- based chatbots in terms of speed and reliability The future scope involves continuous improvements in chatbot responsiveness and intelligence. Ongoing advancements in machine learning, natural language processing and voice recognition contribute to the growing capabilities Of contextual chatbots..

Summary of LR. 3 An Overview of chatbot technology Eleni Adamopoul ou (may 2020) Artificial Intelligence (AI) and Machine Learning (ML),especially Natural Language Understanding (NL U). Advanced AI Integration ,Ethical Considerations ,Real-Time Learning and Adaptation Chat bot creations ADVANTAGES: The advantages highlighted encompass platform independence, instant availability, integration with social graphs, payment services, and notification systems. For developers, advantages include communication reliability, fast development iterations, lack of version fragmentation, and limited design efforts for the interface. DISADVANTAGES: The paper does not discuss the potential impact of false positives or false negatives in the detection of Alzheimer's Disease using the proposed method The future scope involves continuous improvements in chatbot responsiveness and intelligence. Ongoing advancements in machine learning, natural language processing, and voice recognition contribute to the growing capabilities of contextual chatbot exploration of evolving technologies, addressing ethical considerations, and improving user engagement strategies for enhanced chatbot functionality and user experience..

Methodology. Natural Language Processing (NLP) Implementation: Integrate OpenAI's language models for text understanding. Develop algorithms for intent recognition, entity extraction, and context preservation. Real-time Interaction: Design mechanisms for real-time user interaction. Handle interruptions and context switches seamlessly. Voice Synthesis Integration: Integrate a voice synthesis system to convert text responses into natural-sounding speech. Optimize for voice quality, pacing, and intonation to align with conversational context..

Methodology. Requirements Analysis: Identify user expectations and application scenarios. Define functional and non-functional requirements for the VoiceBot system. Technology Stack Selection: Evaluate and choose appropriate technologies for natural language processing, conversational engine, and voice synthesis. Consider factors such as scalability, compatibility, and ease of integration. System Design: Create a detailed system architecture outlining the components and their interactions. Design data flow for user inputs, context management, and voice response generation..

Methodology. Testing and Quality Assurance: Conduct rigorous testing, including unit testing, integration testing, and user acceptance testing. Ensure the system's robustness, security, and adherence to privacy standards. Potential Extensions Exploration: Investigate opportunities for integrating the VoiceBot into specific applications such as virtual assistants, education, customer support, and entertainment. Iterative Development and Feedback: Implement an iterative development process, incorporating user feedback and making continuous improvements to enhance the system's performance and user experience..

Algorithms used. Utilize Transformer-based models like BERT or GPT for intent recognition and context understanding in user inputs. Employ WaveNet or Tacotron for text-to-speech synthesis to achieve high-quality and natural-sounding voice responses. Design the system with a priority queue or event-driven architecture to ensure real-time processing of user inputs and prompt generation of voice responses..

Implementation. def get_payload(text): return def get_generated_audio(text): payload = get_payload(text) generated_response = {} try: response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers) response.raise_for_status() generated_response["type"] = 'SUCCESS' generated_response["response"] = response.text except requests.exceptions.RequestException as e: generated_response["type"] = 'ERROR' try: response_text = json.loads(response.text) if response_text['error_message']: generated_response["response"] = response_text['error_message'] else: generated_response["response"] = response.text except Exception as e: generated_response["response"] = response.text except Exception as e: generated_response["type"] = 'ERROR' generated_response["response"] = response.text return generated_response.

Output.

Output.

Output. Loss Train Loss Validation LOSS 1.42 1.41 1.40 1.39 Accuracy Train Acc. Validation Acc. 0.30 0.25 0.20 0.15.

Conclusion. The project provides the proposed method utilizes Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architecture for the early detection of Alzheimer's Disease from brain MRI images . The CNN and RNN model is more suitable for image processing, especially in image classification, and has shown higher accuracy in predicting Alzheimer's Disease affected-brain vs a normal aging brain . The use of advanced neuroimaging techniques, such as MRI, allows for the extraction of shape features and texture from the Hippocampus region, aiding in the detection of Alzheimer's Disease..

References. Reference: [1] Mendez M F 2012 Early-onset Alzheimer's disease: non amnestic subtypes and type 2 AD Archives of Medical [2] Ballard C, Gauthier S et al 2011 Alzheimer's disease [3] Dayan Peter, Abbott Laurence F et al 2001 Theoretical Neuroscience – Computational and Mathematical Modelling of Neural Systems (MIT press) [4] Hinton Geoffrey E 2011 Machine learning for neuroscience Neural Systems & Circuits.

References. Reference: [5] Meyera Sebastian and Muellera Karsten 2017 Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data Neuro Image Clinical [6] Ahirwar Anamika 2013 Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI I.J.Information Technology and Computer Science [7] Bin Othman Mohd Fauzi, Abdullah Noramalina Bt et al 2011 2011 Fourth Int. Conf. on Modeling, Simulation and Applied Optimization (Kuala Lumpur, Malaysia) MRI Brain Classification using Support Vector Machines.