A Review on Enhancing Naturalness in Text-to-Speech: The Role of Breathing Sound and Emotion Modulation

• Version
• Download 29
• File Size 311.98 KB
• File Count 1
• Create Date 2 July, 2025
• Last Updated 12 July, 2025

Authors : Aishwarya Chandrakant Dindore , Dr.Nilesh Chaudhari

DOI: 10.46335/IJIES.2025.10.6.12

Abstract – The development of speech-processing technology has greatly enhanced communication between humans and computers. With the combination of deep learning, natural language processing (NLP), and artificial intelligence (AI), text-to-speech (TTS) and speech-to-text (STT) systems have been developed. The article also emphasizes how Python libraries help implement these technologies, increasing their usability and effectiveness. One essential kind of communication is speech. By bridging the gap between spoken and written language, TTS and STT technologies enable automatic transcription, virtual assistants, accessibility, and other applications. A rapidly developing area of artificial intelligence, emotion detection is essential to sentiment analysis, psychological research, and human-computer interaction. This review examines several approaches to emotion recognition, such as speech-, text-, and facial expression-based methods. In the domains of human-computer interaction, speech synthesis, and healthcare, breathing sound detection has drawn interest. It is essential for increasing the accuracy of Speech-to-Text (STT) systems and the naturalness of Text-to-Speech (TTS) systems. This paper examines several breathing sound detection techniques, such as deep learning, machine learning, and signal processing. Converting PDFs to audio has become a useful tool in assistive technology, education, and accessibility. This analysis examines several approaches for leveraging Text-to-Speech (TTS) technologies to turn text from PDF documents into speech. The study explores how machine learning, deep learning, and natural language processing (NLP) techniques might improve the precision and naturalness of synthesized speech.