Enchanting Bi-directional Human-Machine Communication Using Deep learning - Based Text- to-speech and Speech- to- text Model

Abstract

Advancements in artificial intelligence have revolutionised human-machine interaction, yet seamless, natural, and bi-directional communication remains a formidable challenge. This study explores the integration of deep learning-based Text-to-Speech (TTS) and Speech-to-Text (STT) models to develop a robust framework for enchanting human-machine communication. Leveraging a hybrid architecture that combines convolutional, recurrent, and attention-based networks, the proposed system converts textual input into highly naturalistic speech and accurately transcribes spoken input into text, achieving near-human performance in both directions. The study employs publicly available datasets, including LJSpeech and LibriSpeech, with advanced preprocessing techniques to normalise audio quality and linguistic variations. Evaluation metrics encompass Word Error Rate (WER), Mean Opinion Score (MOS), Signal-to-Noise Ratio (SNR), and real-time latency, ensuring comprehensive performance assessment. The proposed system demonstrates superior performance compared with state-of-the-art TTS and STT models, achieving a MOS of 4.65/5, WER of 3.2%, and real-time response latency under 200 milliseconds. Additionally, the study examines robustness in noisy environments, highlighting the model’s resilience to acoustic variability and its potential for deployment in real-world applications, including virtual assistants, accessibility tools, and intelligent customer service systems. By integrating TTS and STT in a bi-directional pipeline, the research establishes a framework that not only facilitates natural communication but also supports contextual understanding, adaptive feedback, and conversational continuity. This work contributes significantly to the field of human-computer interaction by providing a scalable, interpretable, and high-fidelity model for bi-directional communication, bridging the gap between synthetic intelligence and human perceptual expectations. The results suggest that deep learning-driven bi-directional models can redefine interactive experiences, enhance accessibility, and set new benchmarks for immersive and responsive AI communication systems.