Text-To-Speech in Voice Assistants: Challenges and Mitigation Strategies

Abstract

Voice Assistants (VAs) have grown rapidly from technological novelties to integral parts of our daily lives to perform tasks like streaming music or news, setting alarms or responding to questions. These virtual conversational agents rely on an intricate combination of technologies, and one of the pivotal components is Text-to-Speech (TTS) synthesis. In this paper, we delve into the technical intricacies of TTS in voice assistants, addressing challenges, solutions, and future directions. VAs like Alexa, Siri and Google Assistant have transformed human-computer interactions. The underpinning TTS technology is crucial for converting text-based information into spoken language, making the interaction more natural and accessible. The synthesis of human-like speech from textual data is a complex and interdisciplinary domain, encompassing fields such as speech signal processing, natural language processing, deep learning, and linguistics. This paper aims to contribute a detailed analysis of TTS in voice assistants, emphasizing not only the theoretical aspects but also the practical implementation and real-world implications. The paper will examine the challenges associated with TTS, considering its technical, linguistic, and user-centric dimensions. The paper will also present mitigation strategies for these challenges. In a world where voice-driven interactions are becoming commonplace, a deep understanding of TTS is vital. By delving into the depths of this technology, we can unlock its full potential and ensure that voice assistants continue to enrich our lives and technical domains