Offline Voice Conversion Model Training via Cloud Extraction
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Solution Overview
Problem
Conventional personalized voice conversion systems require significant storage space and computational resources for effective voice conversion in offline environments, leading to inefficiencies in data retrieval and processing.
Innovation Solution
A system that includes a cloud server and an intelligent device capable of training and utilizing an off-line voice conversion model, allowing the device to convert voices independently when disconnected from the cloud by using computational cache technology and deep neural networks or generative adversarial networks, thereby improving voice conversion quality without increasing storage or computation demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice data is stored in storage space for offline conversion, then voice conversion can be performed offline, but storage space requirements increase significantly
Solution Approach 1:
The patent extracts only the essential voice conversion model parameters and acoustic feature mappings from the complete voice conversion system, storing only these compressed representations in the storage space rather than full voice datasets. This allows offline conversion capability while minimizing storage requirements by taking out only the necessary computational components.
Solution Approach 2:
The system performs preliminary training of the voice conversion model online before offline operation. The model is pre-trained using paired voice data, and only the trained model parameters are stored for offline use. This preliminary action eliminates the need to store large amounts of raw voice data during offline operation, as the conversion logic is already embedded in the pre-trained model.
2Manufacturing precision
If more voice data is stored to improve conversion quality, then conversion effectiveness improves, but computational complexity increases
Solution Approach 1:
The patent transforms the voice conversion problem from manipulating raw voice waveforms to operating in the spectral domain using Mel-frequency cepstral coefficients (MFCCs) and other acoustic features. This parameter transformation simplifies the conversion process by working with compressed acoustic representations rather than high-dimensional raw audio signals, improving quality while reducing computational complexity.
Solution Approach 2:
The system replaces traditional signal processing methods with deep learning-based neural network models for voice conversion. The neural networks automatically learn optimal conversion mappings from training data, substituting manual feature engineering and complex signal processing algorithms with data-driven models that achieve better quality with more efficient computation.
3Manufacturing precision
If voice conversion is performed online using cloud server, then conversion quality is maintained, but system reliability decreases in offline environments
Solution Approach 1:
The system segments the voice conversion functionality into two parts: an online training phase that occurs when connected to the cloud server, and an offline inference phase that operates independently. The model training and updates occur online, while the converted model can be executed offline without requiring continuous cloud connection, thus maintaining both quality and offline reliability.
Solution Approach 2:
The patent creates a local copy of the trained voice conversion model that can be executed on the local device. Instead of requiring the cloud server to be present for every conversion operation, the system copies the trained model parameters to local storage, enabling independent offline operation while maintaining conversion quality through the use of the pre-trained model.
Data Source
AI summary
A personalized voice conversion system includes a cloud server and an intelligent device that communicates with the cloud server. The intelligent device upstreams an original voice signal to the cloud server. The cloud server converts the original voice signal into an intelligible voice signal based on an intelligible voice conversion model. The intelligent device downloads and plays the intelligible voice signal. Based on the original voice signal and the corresponding intelligible voice signal, the cloud server and the intelligent device train an off-line voice conversion model provided to the intelligent device. When the intelligent device stops communicating with the cloud server, the intelligent device converts a new original voice signal into a new intelligible voice signal based on the off-line voice conversion model and plays the new intelligible voice signal.