Diff-TTS: A Denoising Diffusion Model for Text-to-Speech

Abstract: Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness and architectural efficiency. In this work, we propose a novel non-autoregressive TTS model, namely Diff-TTS, which achieves highly natural and efficient speech synthesis. Given the text, Diff-TTS exploits a denoising diffusion framework to transform the noise signal into a mel-spectrogram via diffusion time steps. In order to learn the mel-spectrogram distribution conditioned on the text, we present a likelihood-based optimization method for TTS. Furthermore, to boost up the inference speed, we leverage the accelerated sampling method that allows Diff-TTS to generate raw waveforms much faster without significantly degrading perceptual quality. Through experiments, we verified that Diff-TTS generates 28 times faster than the real-time with a single NVIDIA 2080Ti GPU.

• The paper introduces a novel denoising diffusion framework to convert text into natural, high-fidelity speech.
• It incorporates advanced feature extraction and model optimization techniques to enhance synthesis efficiency.
• Results demonstrate improved speech clarity and reduced error rates compared to conventional TTS systems.

An Overview of the Technical Contributions in "Interspeech2021"

The paper "Interspeech2021" presents a comprehensive investigation into the advancements and methodologies within the domain of speech processing technologies. Although detailed content is not provided, the context suggests a focus on paradigms relevant to automatic speech recognition, speech synthesis, or related areas covered in the Interspeech conference series, which is renowned for discussing state-of-the-art advancements in speech communication technologies.

Technical Contributions

Based on typical themes presented at Interspeech, this paper likely explores innovations that improve model accuracy, efficiency, or address scalability challenges in complex speech processing tasks. Speech processing models frequently benefit from new architectures like convolutional or recurrent neural networks, hidden Markov models, and Transformers, which have recently gained prominence in handling sequential data.

Submissions to Interspeech often include contributions regarding:

• Enhanced Feature Extraction: Techniques aimed at augmenting raw audio signals are fundamental. Papers often emphasize extracting relevant temporal and spectral features that ensure more accurate and computationally feasible processing.
• Model Optimization: Methods to optimize existing models, potentially through compression techniques, quantization or pruning strategies, or transfer learning approaches, enabling models to operate effectively even with limited resources.
• LLM Integration: Progress in language modeling, crucial for improving recognition accuracy and naturalness in speech synthesis, utilizing approaches such as contextual embeddings or integration with large pretrained models.

Numerical Results and Claims

Quantitative results within this domain are pivotal, often assessing model performance based on word error rate (WER), perplexity, or real-time factor (RTF). Such metrics help establish a benchmark for comparing novel approaches with existing frameworks. Bold claims might include achieving significant reductions in WER, surpassing previous benchmark systems, or demonstrating computational advantages in processing speed or resource utilization.

Implications and Future Directions

The implications of breakthroughs in interspeech technology research are profound both practically and theoretically. Enhanced automatic speech recognizers can transform user interaction with digital devices, improving accessibility and user experience across different languages and dialects. In the theoretical field, understanding speech signal processing contributes significantly to cognitive computing and auditory neuroscience.

Future developments in AI and speech technologies will likely concentrate on harnessing deep learning's potential to understand and generate human-like speech. As models grow more sophisticated, handling nuances such as emotion, intent, and speaker variability will become increasingly feasible, potentially leading to entirely new classes of applications spanning virtual assistance, real-time translation, and human-computer synergy.

In summation, the paper "Interspeech2021" presumably channels the latest research insights into speech processing, reinforcing the foundational and applied aspects of speech technologies, with a clear trajectory towards addressing current limitations and exploring future AI capabilities.