Advancements in Speech Synthesis and Recognition Technologies through Machine Learning and Phonetics

Abstract

ABSTRACT

This research explores advancements in speech synthesis and recognition technologies, focusing on the integration of machine learning and phonetics. As voice-based interfaces become increasingly prevalent in various applications, understanding the interplay between phonetic principles and machine learning algorithms is essential for enhancing the accuracy and naturalness of speech technologies. This study employs a comprehensive analysis of recent developments in deep learning techniques, including neural networks and reinforcement learning, to investigate their impact on synthesizing human-like speech and improving recognition systems. Key components such as prosody, intonation, and articulation are examined to identify how phonetic features can be effectively modeled and incorporated into these systems. The findings indicate that leveraging phonetic insights alongside machine learning frameworks significantly enhances the performance of both speech synthesis and recognition, resulting in more intuitive and responsive user interactions. Additionally, the research discusses the implications of these advancements for diverse fields, including accessibility, telecommunications, and artificial intelligence. Ultimately, this study contributes to the understanding of how interdisciplinary approaches can drive innovation in speech technologies, paving the way for more sophisticated and human-centric voice interfaces.

Keywords:Keywords: speech synthesis, speech recognition, machine learning, phonetics, deep learning, neural networks, prosody, artificial intelligence.