LatentSpeech: Latent Diffusion for Text-To-Speech Generation (2412.08117v1)

Abstract: Diffusion-based Generative AI gains significant attention for its superior performance over other generative techniques like Generative Adversarial Networks and Variational Autoencoders. While it has achieved notable advancements in fields such as computer vision and natural language processing, their application in speech generation remains under-explored. Mainstream Text-to-Speech systems primarily map outputs to Mel-Spectrograms in the spectral space, leading to high computational loads due to the sparsity of MelSpecs. To address these limitations, we propose LatentSpeech, a novel TTS generation approach utilizing latent diffusion models. By using latent embeddings as the intermediate representation, LatentSpeech reduces the target dimension to 5% of what is required for MelSpecs, simplifying the processing for the TTS encoder and vocoder and enabling efficient high-quality speech generation. This study marks the first integration of latent diffusion models in TTS, enhancing the accuracy and naturalness of generated speech. Experimental results on benchmark datasets demonstrate that LatentSpeech achieves a 25% improvement in Word Error Rate and a 24% improvement in Mel Cepstral Distortion compared to existing models, with further improvements rising to 49.5% and 26%, respectively, with additional training data. These findings highlight the potential of LatentSpeech to advance the state-of-the-art in TTS technology

• The paper introduces an innovative latent diffusion model for TTS that bypasses traditional Mel-spectrograms, drastically reducing computational complexity.
• The paper achieves a 25% reduction in WER and a 24% improvement in MCD, demonstrating superior performance on a challenging Chinese speech dataset.
• The paper highlights enhanced training efficiency and scalability, paving the way for real-time speech synthesis on resource-constrained devices.

An Analysis of LatentSpeech: Latent Diffusion for Text-To-Speech Generation

The paper "LatentSpeech: Latent Diffusion for Text-To-Speech Generation" presents an innovative approach to Text-to-Speech (TTS) synthesis through the use of latent diffusion models. This work leverages the power of diffusion-based generative AI, which has demonstrated superiority over alternative techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in various domains, including image and natural language processing. Here, diffusion models' potential is extended into the relatively under-explored field of speech generation.

Core Contributions

1. Novel Use of Latent Diffusion in TTS: LatentSpeech distinguishes itself by utilizing latent embeddings directly in the audio space, circumventing the traditional reliance on Mel-spectrograms (MelSpecs) as an intermediate representation. This innovation reduces the computational complexity by shrinking the intermediate representation dimension to just 5% of what is needed with MelSpecs.
2. Improved Performance Metrics: Experimental results demonstrate significant improvements in both Word Error Rate (WER) and Mel Cepstral Distortion (MCD). Specifically, LatentSpeech outperforms existing models with a 25% reduction in WER and a 24% reduction in MCD when trained on a smaller dataset. These improvements become even more pronounced with larger datasets, highlighting the scalability of the approach.
3. Advantageous Computational Efficiency: By operating in latent spaces, LatentSpeech simplifies the processes required for TTS encoding and vocoding, leading to high-quality speech generation with reduced computational and parameter demands.

Methodological Insights

The authors employ a diffusion-based probabilistic generative model that operates by denoising a variable over a reverse Markov chain to match the latent embedding distribution. The process starts with encoding speech into a latent space using Autoencoders, followed by a TTS encoder based on the StyleSpeech architecture. A novel Conditional Denoiser is used, which employs bidirectional dilated convolution kernels within a series of residual blocks to iteratively reconstruct the speech waveform from the latent embeddings.

The model's effectiveness is further illustrated via a comprehensive breakdown of its architecture. Notable are the adaptations that include Pseudo Quadrature Mirror Filters (PQMF) to conduct multi-band decomposition of the raw audio signal, facilitating better handling of the latent space encoding.

Empirical Evaluation

The evaluation is conducted using a Chinese speech dataset, notable for its pronunciation complexities. LatentSpeech demonstrated substantial benefits in data efficiency, requiring substantially fewer dimensions than MelSpecs for equivalent speech information capture. Different model configurations reveal that increasing the dataset size consistently enhances performance metrics, underlining the robustness and adaptability of LatentSpeech in multiple training scenarios.

Implications and Future Directions

The results underscore a tangible advancement towards efficient TTS systems, promising enhanced speech synthesis quality with reduced computational overhead. The potential implications of this approach are significant, particularly for applications requiring real-time speech synthesis on resource-constrained devices. Additionally, the success of latent diffusion methods as applied in LatentSpeech could influence future generative AI efforts across a multitude of audio-visual domains.

Future investigations could explore further optimization of latent embeddings and diffusion processes, as well as cross-lingual adaptations to validate the model’s performance across diverse languages and dialects. Alternate audio features and noise reduction strategies might also be pursued to refine output quality and broaden the scope of audio synthesis applications.

In summation, LatentSpeech offers a compelling direction for the evolution of TTS technology, providing both a practical and scalable solution to challenges faced by traditional speech synthesis methodologies.