Revolutionizing Speech Synthesis: Deep Neural Networks Propel Text-to-Speech and Singing Voice Advancements

The rapid expansion of human-machine interaction and speech synthesis technologies has propelled text-to-speech (TTS) and singing voice synthesis to the forefront of artificial intelligence.

Advances in deep neural networks have played a pivotal role in various aspects of human-machine communication, making it increasingly sophisticated and human-like.

A typical speech synthesis process relies on a two-stage pipeline consisting of an acoustic model and a vocoder. The acoustic model is responsible for translating text input into a mel-spectrogram, a time-frequency representation of the speech signal. The vocoder then takes the mel-spectrogram and converts it into an audible waveform. This process ensures that the quality of the synthesized speech is both accurate and engaging.

Some of the industry-standard methods, such as Tacotron, DurIAN, and FastSpeech, employ convolutional neural networks (CNNs) and transformer models to excel at TTS and singing voice synthesis applications. Their success has stemmed from the deep learning architectures’ ability to capture complex patterns, leading to higher quality voice synthesis.

Recently, speech synthesis researchers have shifted their focus towards diffusion model approaches, aiming to overcome some limitations of the current state-of-the-art TTS models. A diffusion model involves two processes: the diffusion process, where noise is gradually introduced into a mel-spectrogram, and the reverse process, wherein the noise is removed. However, these models have been hindered by the inference speed, as multiple iterations are required in the reverse process.

To overcome this limitation, new advancements in diffusion models have emerged, such as Grad-TTS. By solving the reverse stochastic differential equation (SDE) for noise to mel-spectrogram transformation, Grad-TTS delivers high-quality synthesis while maintaining efficient inference speeds. Progressive distillation and the Prodiff model also aim to reduce the time spent on the sampling process, further improving the efficiency of TTS models.

In addition, DiffGAN-TTS harnesses the power of adversarially-trained models for more effective voice synthesis, leading to increasingly engaging and realistic output. Meanwhile, the ResGrad model focuses on estimating the prediction residual from pre-trained FastSpeech2 and ground truth using diffusion models, further refining the quality of synthesized speech.

In summary, the quest for excellence in speech synthesis revolves around three primary goals: delivering excellent audio quality, ensuring expressiveness and naturalness, and achieving efficient inference speed. The integration of deep neural networks and continuous research advancements in TTS and singing voice synthesis models promise a future where human-machine interaction will feel increasingly seamless and intuitive. As researchers relentlessly push the boundaries of what is possible, it becomes evident that the harmonious collaboration between humans and machines is closer than ever before.