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=== Synthesis === From the articles on non-language-specific text-to-speech (TTS) synthesis highlights several emerging trends and debates within the field of voice technology. The articles reviewed contribute to a comprehensive understanding of the state-of-the-art in TTS naturalness, spanning advanced models and theories that aim to bridge the gap between synthetic and human speech. '''Emerging Trends:''' 1. Advancements in Model Architecture: A significant trend is the development of advanced TTS models, such as NaturalSpeech, Make-an-Audio, HiFi-GAN, and SpeedySpeech, which leverage innovative techniques like variational autoencoders, prompt-enhanced diffusion models, adversarial training, and efficient network architectures. These models aim to improve the naturalness and expressivity of synthetic speech, achieving closer approximation to human speech quality. 2. Integration of Linguistic and Emotional Information: There is a growing emphasis on incorporating linguistic features and emotional expressivity into TTS systems. Studies like the one by Peiró-Lilja & Farrús, and Cai et al. demonstrate the potential of enhancing speech naturalness and emotional expressivity by embedding linguistic cues and leveraging emotion-unlabeled datasets with cross-domain speech emotion recognition models. This approach points to a nuanced understanding of speech production, where prosody, context, and emotional tone play crucial roles. 3. Exploration of Vocal Persona and Contextual Factors: The study by Noufi, May, & Berger introduces the concept of vocal persona, highlighting the influence of context on vocal expression and identity. This reflects an acknowledgment of the complexity of human speech, where individuals adapt their vocal style to different social contexts. Integrating such contextual and persona-based nuances into TTS systems could lead to more sophisticated and contextually aware speech synthesis. '''Debates:''' Quality vs. Complexity: Despite advancements, a recurring challenge is the trade-off between improving speech quality and managing the complexity and computational demands of TTS models. Models like HiFi-GAN and SpeedySpeech address this by optimizing for efficiency and fidelity, yet questions remain about the balance between model simplicity and the ability to capture the rich variability of human speech. In conclusion, the field of TTS is witnessing rapid advancements and facing complex challenges. The synthesis of findings from the reviewed articles underscores the importance of multidisciplinary approaches that integrate technical innovations with insights from linguistics and psychology to advance towards more natural, expressive, and ethically developed TTS technologies.
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