Direct Neural Speech Phase Prediction Model Outperforms Iterative Algorithms and Waveform Reconstruction Methods

Kernekoncepter

Direct neural speech phase prediction model outperforms iterative algorithms and waveform reconstruction methods in terms of precision, efficiency, and robustness.

Resumé

The content introduces a novel neural speech phase prediction model that directly predicts wrapped phase spectra from amplitude spectra. The model utilizes a parallel estimation architecture and anti-wrapping losses to achieve low-latency streamable phase prediction. Experimental results show superior performance compared to iterative algorithms and waveform reconstruction methods in terms of phase prediction precision, efficiency, and robustness. The model demonstrates high generation efficiency and quality of synthesized speech, making it suitable for specific speech generation tasks.

Statistik

The proposed model achieves 19.6x real-time generation on a CPU with low latency.
The NSPP obtained the highest SNR and the lowest F0-RMSE among all methods for all three tasks.
The NSPP exhibited a latency of 330 ms.

Citater

"Our proposed model outperforms the iterative phase estimation algorithms and neural network-based phase prediction methods in terms of phase prediction precision, efficiency, and robustness."
"Compared with the HiFi-GAN vocoder-based waveform reconstruction method, our proposed model demonstrates a significant efficiency advantage while maintaining the same quality of synthesized speech."

Vigtigste indsigter udtrukket fra

Low-Latency Neural Speech Phase Prediction based on Parallel Estimation Architecture and Anti-Wrapping Losses for Speech Generation Tasks

by Yang Ai,Zhen... kl. arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17378.pdf

Dybere Forespørgsler

How can the direct prediction of wrapped phase spectra benefit other speech generation tasks beyond the ones mentioned in the article

Direct prediction of wrapped phase spectra can benefit other speech generation tasks by improving efficiency, precision, and robustness.

Efficiency: Direct prediction eliminates the need for iterative algorithms, reducing computational complexity and speeding up the generation process. This can lead to real-time or near-real-time performance in various applications.

Precision: By directly predicting the phase spectra, the model can achieve higher accuracy in reconstructing the speech waveform. This results in more natural and high-fidelity speech output, enhancing the overall quality of the generated speech.

Robustness: Direct prediction models can be more robust when faced with degraded or noisy input data. They can learn to adapt to variations in the input amplitude spectra, leading to more stable and reliable performance across different conditions.

Adaptability: The direct prediction approach can be applied to a wide range of speech generation tasks beyond the ones mentioned in the article, such as voice cloning, voice conversion, and speech recognition. It can enhance the performance and efficiency of these tasks by providing accurate phase information for waveform reconstruction.

What are the potential limitations or drawbacks of using a direct neural speech phase prediction model compared to iterative algorithms

While direct neural speech phase prediction models offer several advantages, they also have potential limitations compared to iterative algorithms:

Complexity: Direct prediction models may require more complex architectures and training strategies to effectively capture the intricate relationships between amplitude and phase spectra. This complexity can make the models harder to interpret and optimize.

Training Data: Direct prediction models may require a large amount of high-quality training data to learn the complex mapping between amplitude and phase spectra. Insufficient or noisy training data can lead to suboptimal performance.

Generalization: Direct prediction models may struggle to generalize to unseen or noisy data, as they rely heavily on the training data distribution. They may not be as robust as iterative algorithms in handling variations in input data.

Latency: Depending on the model architecture and training approach, direct prediction models may have higher latency compared to iterative algorithms, especially when incorporating non-causal convolutions for better performance.

Error Propagation: Errors in the predicted phase spectra can propagate through the waveform reconstruction process, leading to artifacts or distortions in the synthesized speech. Iterative algorithms may offer better error correction mechanisms in this regard.

How might advancements in neural speech phase prediction impact the field of speech synthesis in the future

Advancements in neural speech phase prediction can have a significant impact on the field of speech synthesis in the following ways:

Improved Speech Quality: More accurate and precise phase prediction can lead to higher-quality synthesized speech with enhanced naturalness and clarity. This can result in more realistic and engaging voice applications.

Real-Time Applications: Efficient neural phase prediction models can enable real-time speech synthesis for applications like virtual assistants, voice-controlled devices, and telecommunication systems. This can enhance user experience and interaction.

Robustness and Adaptability: Advanced neural models can improve the robustness of speech synthesis systems by handling variations in input data and environmental conditions. They can adapt to different speakers, languages, and accents more effectively.

Personalization and Customization: Neural phase prediction models can facilitate personalized speech synthesis by capturing individual speaking styles, emotions, and characteristics. This can lead to more tailored and engaging voice experiences for users.

Innovation in Voice Technology: Advancements in neural speech phase prediction can drive innovation in voice technology, enabling new applications such as emotional speech synthesis, multilingual voice interfaces, and improved accessibility features for individuals with speech impairments.