DRL VXV
DRL VXV
DRL VXV
Text-to-Speech
Vo Xuan Vuong
June 24, 2024
Abstract
1 Introduction
Text-to-speech (TTS) is a crucial technology that enables the conversion of written text into spoken
language, with applications in various domains such as assistive technology, e-learning, and virtual
assistants. Despite significant progress in TTS over the years, producing high-quality synthetic speech
remains a challenging task, especially in the context of more expressive and natural-sounding speech.
Traditional TTS approaches often rely on rule-based or statistical models that have limited ability
to capture the complex relationship between text and speech. The emergence of deep learning has
revolutionized the field of TTS, with neural network-based models demonstrating superior performance
in generating more intelligible and natural-sounding speech. These deep learning models are capable
of automatically learning powerful feature representations from large datasets, enabling them to better
capture the nuances and patterns in human speech.
However, even with the advancements in deep learning-based TTS, there are still opportunities to
further improve the quality and expressiveness of the generated speech. One promising approach is
the integration of deep reinforcement learning (DRL) techniques, which can enable the TTS model
to learn optimal policies through interactions with the environment and feedback signals. DRL-based
TTS models have the potential to learn more contextual and personalized speech patterns, leading to
more natural and engaging synthetic speech.
This research aims to explore the application of deep reinforcement learning to enhance the quality
of text-to-speech systems. By leveraging the representation power of deep learning and the adaptive
learning capabilities of reinforcement learning, we seek to develop improved TTS models that can
generate more expressive and human-like speech. The proposed approach will be evaluated on a
Vietnamese dataset, contributing to the advancement of TTS technology in the Vietnamese language
domain..
2 Related Work
The field of object detection has seen remarkable advancements over the past decade, primarily driven
by deep learning techniques. Traditional approaches relied on handcrafted features and simple clas-
sifiers, which have been largely outperformed by deep neural networks (DNNs). Seminal works such
as R-CNN and Faster R-CNN introduced CNN-based object detection frameworks that significantly
improved accuracy and speed compared to previous methods.
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Rapid advancements in deep learning have continued to revolutionize the field of TTS. Neural
network-based TTS models, such as Tacotron and Transformer-based architectures, have achieved
significant improvements in quality, expressiveness, and speaker adaptability. More recently, TTS
models based on Generative Adversarial Networks (GANs) have also shown the ability to generate
highly realistic synthetic speech that is difficult to distinguish from natural human voice.
3 Environment
The environment consists of a text-to-speech synthesis system that interacts with a speech emotion
recognition (SER) model. The ETTS system generates speech from given text inputs, and the SER
model evaluates the emotional content of the generated speech
4 Agent
The agent in this DRL framework is the ETTS model, which is built on the Global Style Tokens (GST)-
based Tacotron architecture. The agent’s task is to generate mel-spectrograms from text inputs that
are not only natural-sounding but also emotionally expressive.
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• GST Module: Generates style tokens corresponding to different emotions.
• Decoder: Produces mel-spectrograms from text and emotion embeddings.
5 Reward
The reward function is based on the accuracy of the SER model in recognizing the intended emotion
from the synthesized speech. This ensures that the ETTS model not only produces natural-sounding
speech but also accurately conveys the specified emotion.
• Emotion Recognition: Training agents to recognize and classify emotions from speech signals.
• Speech Synthesis: Enhancing the emotional expression in synthesized speech by DRL agents.
• Human-Robot Interaction: Improving the responsiveness of robots to human emotions.
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7 Algorithms
Several DRL algorithms can be utilized for this project, each with unique advantages and suitable
applications. Below are some key algorithms:
8 Conclusion
This report presents a detailed overview of using deep reinforcement learning for emotional text-to-
speech synthesis. The combination of ETTS with an SER model in a reinforcement learning framework
allows for improved emotion discriminability in synthesized speech. The use of the RAVDESS dataset
provides a rich source of emotional speech data for training and evaluation. The inclusion of various
DRL algorithms offers multiple approaches to optimizing the performance of the ETTS system.
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9 References
• Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis,
D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-
533.
• Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy
optimization algorithms. arXiv preprint arXiv:1707.06347.
• Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., ... & Wierstra, D. (2015).
Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.