RealPRNet Neural Network for Real-Time Speech Animation
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Solution Overview
Problem
Current technologies face challenges in generating high-quality, real-time speech animation with low latency, especially when audio is the only available input, due to limitations in phoneme recognition accuracy and latency, which affects the realism and scalability of virtual characters in applications like online conferences and gaming.
Innovation Solution
A method and device using a neural network architecture called RealPRNet, which processes audio signals to recognize phonemes by transforming them into frequency-domain features, applying convolutional neural networks, long short-term memory networks, and hidden Markov models to generate speech animation, balancing accuracy and latency through temporal and spatial correlation modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional phoneme recognition methods are used with audio as the only input, then resource constraints are satisfied, but phoneme recognition accuracy and latency performance deteriorate
Solution Approach 1:
The audio signal is segmented into frames, and each frame is processed independently through the neural network to produce phoneme predictions. This segmentation allows for real-time processing while maintaining accuracy by analyzing short temporal segments with sufficient context.
Solution Approach 2:
The system performs preliminary feature extraction by transforming audio signals into frequency-domain features (MFCCs) before feeding them to the neural network. This preliminary processing prepares the data in advance, reducing the computational burden during real-time inference and lowering latency.
2Manufacturing precision
If high-fidelity realistic speech animation is produced, then animation quality is improved, but production cost and data requirements increase significantly
Solution Approach 1:
The system creates a virtual avatar that copies and replicates human speech animation characteristics. By training the neural network on existing audio-visual data, the system learns to generate realistic mouth movements and facial expressions that copy human speech patterns without requiring expensive real-time recording sessions.
Solution Approach 2:
The patent replaces traditional mechanical animation systems with a neural network-based approach. Instead of using complex mechanical animators or manual keyframing, the system uses deep learning models to directly transform audio inputs into corresponding visual outputs, significantly reducing production complexity and cost.
3Productivity
If real-time speech animation is implemented with audio as the only input, then scalability is improved, but phoneme recognition performance deteriorates due to resource limitations
Solution Approach 1:
The system changes the parameter space by transforming audio signals into frequency-domain features (MFCCs) with specific parameter transformations. This transformation compresses the information into a more efficient representation that can be processed faster by the neural network, enabling real-time processing while maintaining accuracy.
Solution Approach 2:
The neural network architecture is designed to be universal, handling multiple tasks including phoneme recognition, speech synthesis, and animation generation within a single unified model. This multi-functionality allows the system to scale efficiently across different applications without requiring separate specialized models for each task.
Data Source
AI summary
A method for generating speech animation from an audio signal includes: receiving the audio signal; transforming the received audio signal into frequency-domain audio features; performing neural-network processing on the frequency-domain audio features to recognize phonemes, wherein the neural-network processing is performed using a neural network trained with a phoneme dataset comprising of audio signals with corresponding ground-truth phoneme labels; and generating the speech animation from the recognized phonemes.