Evaluating Text-to-Speech and Audio Codec Performance for Voice Communication in Resource-Constrained Networks

Abstract

Voice communications are valued for their ease of use and the rich information they provide, offering an immediate, clear, and efficient way to convey messages. However, ensuring the clarity and reliability of voice communications in low-bandwidth networks poses a technical challenge. This research explores the efficacy of Text-to-Speech (TTS) models and vocoder combinations versus traditional audio codecs in low-bandwidth networks, highlighting considerations for voice clarity and network resource management. Traditional audio codecs in bandwidth-limited environments often compromise audio quality and reliability. On the contrary, TTS models, supported by the advancements in deep and machine learning, present a potential alternative. Through a methodical comparison using various evaluation metrics, the study aims to offer valuable insights into their comparative impacts on audio quality and network behavior.