Avatar Speech Animation Timing Refinement Using ML Offsets

The patent replaces the manual mechanical adjustment process with an automated machine learning-based system. The ML model predicts alignment offsets between ASR phoneme timing and actual audio timing, automatically refining the phoneme timing without human intervention. This substitution of manual mechanical work with automated intelligent systems resolves the contradiction by maintaining high alignment accuracy while eliminating time-consuming manual adjustments.

The patent introduces an intermediary alignment offset prediction mechanism that bridges ASR phoneme timing and actual audio timing. The ML model acts as an intermediary layer that processes ASR output and generates corrected phoneme timing by predicting alignment offsets. This intermediary system enables automatic high-precision alignment without requiring manual review, resolving the time consumption issue while maintaining accuracy.

The patent replaces resource-intensive manual review processes with an automated ML-based system. The machine learning model, once trained, efficiently predicts alignment offsets with minimal computational overhead compared to manual review. This substitution reduces system resource consumption by eliminating the need for human operators while maintaining or improving alignment accuracy through automated processing.

The patent performs preliminary training of the ML model using synthesized speech data with known ground truth phoneme timing. This preliminary action creates a pre-trained model that can automatically predict alignment offsets without requiring manual adjustment for each new audio stream. The one-time training investment reduces ongoing system resource consumption by enabling automated high-precision alignment for subsequent processing tasks.

The patent performs preliminary training of the ML model using synthesized speech data with known ground truth phoneme timing before deployment. This preliminary action creates a pre-trained model with learned alignment patterns that can accurately predict phoneme timing offsets. By investing computational resources in advance for training, the system achieves both high processing efficiency during inference and maintains alignment accuracy through learned patterns from diverse training data.

The patent uses synthesized speech data as a copy or approximation of real speech to generate training data with known ground truth phoneme timing. The synthesis system produces audio with perfectly known phoneme boundaries, creating ideal training examples. This copying approach enables the ML model to learn accurate alignment patterns without requiring extensive manual annotation of real speech data, maintaining accuracy while improving productivity.