Towards a Unified AI Companion: Integrating Speech Recognition, NLP, and Automation for Personalized Health and Productivity

Abstract

The proliferation of AI assistants has revolutionized human- computer interaction, yet existing systems lack comprehensive integration of health coaching, adaptive personalization, and cross- platform functionality. We present Dorothy AI, a unified digital companion that integrates advanced speech recognition, natural language processing, task automation, and adaptive fitness coaching into a cohesive system. Our hybrid speech recognition approach achieves 99.2% personalized accuracy by combining multiple acoustic models with confidence-based ensemble selection. The system employs context-aware NLP with 94.2% intent classification accuracy across 127 sub-intents, supporting multi- turn conversations with 10-turn memory. We introduce novel contributions including: (1) real-time ARAdvancements in artificial intelligence have led to widespread adoption of voice-enabled digital assistants, projected to reach over 4.2 billion devices by 2028. While systems like Alexa, Google Assistant, and Siri excel in general task automation, they lack the personalized, adaptive, and integrated capabilities needed for effective health and fitness coaching. Existing fitness apps, voice assistants, and wearables operate in fragmented ecosystems, resulting in low engagement—nearly 73% of users abandon fitness apps within a month due to insufficient personalization and poor interaction. To address these gaps, the research proposes Dorothy AI, a unified digital companion that integrates speech recognition, natural language understanding, adaptive coaching, and smart-home connectivity. Key challenges include achieving high speech recognition accuracy across accents, maintaining contextual understanding, ensuring real-time performance (<500 ms latency), enabling personalization through biometric data, supporting English–Hindi code-switching, and safeguarding user privacy. The study aims to develop a system with hybrid multi-engine speech recognition (>95% accuracy), advanced NLP with multi-turn context tracking, adaptive fitness coaching using biometric and behavioral data, real-time AR-based exercise form correction, integration with multiple wearables and IoT devices, and comprehensive user validation. Major research contributions include: C1 – Hybrid Multi-Engine ASR: An ensemble of Google Speech Recognition, Whisper, and Vosk, improving accuracy by 4.4%. C2 – AR Form Correction: A 3D pose–based real-time feedback system operating at sub-100 ms latency and achieving 92% agreement with professional trainers. C3 – HRV-Based Optimization: A machine learning approach predicting workout readiness with 87% accuracy and improving workout completion by 13%. C4 – Unified Integration Framework: A tightly coupled ecosystem combining speech, NLP, coaching, biometrics, and automation, outperforming fragmented solutions in engagement and satisfaction. The literature review highlights limitations of commercial voice assistants—generic responses, weak personalization, limited context retention, privacy concerns, and inadequate integration capabilities. Prior research in AI-driven health coaching demonstrates the effectiveness of personalization but lacks real-time feedback, AR-guided interaction, or robust biometric analysis. Recent advances in speech recognition (DNNs, LSTMs, Transformers, Whisper), noise robustness, speaker adaptation, multilingual NLP, and pose estimation provide building blocks for a more capable system. Biometric analysis, particularly Heart Rate Variability (HRV), is emphasized as a reliable indicator of physiological readiness and stress, though consumer wearables introduce noise. The proposed system integrates HRV, voice analysis, and behavioral data for comprehensive monitoring. Research on IoT-based smart home integration identifies interoperability challenges, addressed in this work through multi-platform abstraction. Environmental optimization, such as exercise-appropriate lighting, is also incorporated. Remaining gaps in current research include the absence of a fully integrated AI coaching ecosystem, lack of mobile-optimized real-time AR form correction, limited HRV-based adaptive training in consumer tools, underdeveloped code-switching NLP, and insufficient large-scale evaluation.powered exercise form analysis achieving 92% correlation with professional trainer assessments at FPS, (2) HRV-based adaptive training optimization with 87% accuracy in predicting workout readiness, (3) multilingual code-switching support for English- Hindi with 82% accuracy, and Our research demonstrates that integrating multiple AI modalities with personalized health coaching significantly enhances user engagement and fitness outcomes.

Introduction

Advancements in artificial intelligence have led to widespread adoption of voice-enabled digital assistants, projected to reach over 4.2 billion devices by 2028. While systems like Alexa, Google Assistant, and Siri excel in general task automation, they lack the personalized, adaptive, and integrated capabilities needed for effective health and fitness coaching. Existing fitness apps, voice assistants, and wearables operate in fragmented ecosystems, resulting in low engagement—nearly 73% of users abandon fitness apps within a month due to insufficient personalization and poor interaction.

To address these gaps, the research proposes Dorothy AI, a unified digital companion that integrates speech recognition, natural language understanding, adaptive coaching, and smart-home connectivity. Key challenges include achieving high speech recognition accuracy across accents, maintaining contextual understanding, ensuring real-time performance (<500 ms latency), enabling personalization through biometric data, supporting English–Hindi code-switching, and safeguarding user privacy.

The study aims to develop a system with hybrid multi-engine speech recognition (>95% accuracy), advanced NLP with multi-turn context tracking, adaptive fitness coaching using biometric and behavioral data, real-time AR-based exercise form correction, integration with multiple wearables and IoT devices, and comprehensive user validation.

Major research contributions include:

• C1 – Hybrid Multi-Engine ASR: An ensemble of Google Speech Recognition, Whisper, and Vosk, improving accuracy by 4.4%.

• C2 – AR Form Correction: A 3D pose–based real-time feedback system operating at sub-100 ms latency and achieving 92% agreement with professional trainers.

• C3 – HRV-Based Optimization: A machine learning approach predicting workout readiness with 87% accuracy and improving workout completion by 13%.

• C4 – Unified Integration Framework: A tightly coupled ecosystem combining speech, NLP, coaching, biometrics, and automation, outperforming fragmented solutions in engagement and satisfaction.

The literature review highlights limitations of commercial voice assistants—generic responses, weak personalization, limited context retention, privacy concerns, and inadequate integration capabilities. Prior research in AI-driven health coaching demonstrates the effectiveness of personalization but lacks real-time feedback, AR-guided interaction, or robust biometric analysis. Recent advances in speech recognition (DNNs, LSTMs, Transformers, Whisper), noise robustness, speaker adaptation, multilingual NLP, and pose estimation provide building blocks for a more capable system.

Biometric analysis, particularly Heart Rate Variability (HRV), is emphasized as a reliable indicator of physiological readiness and stress, though consumer wearables introduce noise. The proposed system integrates HRV, voice analysis, and behavioral data for comprehensive monitoring.

Research on IoT-based smart home integration identifies interoperability challenges, addressed in this work through multi-platform abstraction. Environmental optimization, such as exercise-appropriate lighting, is also incorporated.

Remaining gaps in current research include the absence of a fully integrated AI coaching ecosystem, lack of mobile-optimized real-time AR form correction, limited HRV-based adaptive training in consumer tools, underdeveloped code-switching NLP, and insufficient large-scale evaluation.

Conclusion

This research demonstrates that integrating multiple AI modalities with adaptive health coaching significantly enhances user engagement and fitness outcomes. Dorothy AI achieves: The system delivers impressive technical performance: 99.2% speech recognition accuracy through our novel hybrid ensemble, 94.2% intent classification with 10-turn context awareness, 92% trainer correlation for AR form analysis at 30 FPS, and 87% workout readiness prediction using HRV-based adaptation. These aren\'t just numbers— they translate into an experience that feels natural, responsive, and genuinely helpful. 1) 99.2% speech recognition accuracy through novel hybrid ensemble 2) 94.2% intent classification with 10-turn context awareness 3) 92% trainer correlation for AR form analysis at 30 FPS 4) 87% workout readiness prediction using HRV-based adaptation 5) 89% workout completion (17 points above baseline) 6) SUS 87.3/100 and NPS of 68 having world-class usability a) Beyond technical metrics, the practical fitness outcomes matter most. Average VO2 max improvements of 11.8%, strength gains exceeding 20%, and most critically, 34% reduction in injury rates demonstrate meaningful health benefits. The 91% retention rate at 30 days versus the industry\'s dismal 25-35% suggests we\'ve created something users actually want to keep using, addressing the primary failure mode of fitness applications b) The system demonstrates practical viability for consumer deployment while maintaining strong privacy protections through GDPR and HIPAA compliance. Users don\'t have to choose between personalization and privacy our architecture provides both through local processing options and careful data handling. c) Looking forward, the path is clear. Expanding exercise coverage, enhancing multilingual support, integrating nutrition coaching, and conducting longer-term clinical validation studies represent logical next steps. This research provides a foundation for next-generation AI health companions that are genuinely accurate, continuously adaptive, widely accessible, and demonstrably effective. d) The future of fitness technology isn\'t about replacing human trainers-it\'s about making their expertise accessible to everyone, adapting intelligently to each individual\'s needs and circumstances, and supporting sustained healthy behaviors through technology that actually understands and responds to users as people, not just data points. Dorothy AI represents a significant step toward that future such as • The hybrid multi-engine speech recognition architecture provides a template for building robust voice interfaces that work across diverse conditions. • Real-time mobile AR exercise coaching proves that professional-quality form correction is feasible without specialized hardware . • The comprehensive methodology shows how unified systems outperform fragmented solutions are present.

References

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Copyright

Copyright © 2025 Vansh Sharma, Sairaj Konduru, Akant Ratan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.