Disentangled Speech Audio Generation for Video Games
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Solution Overview
Problem
Existing speech audio generation systems for video games lack the ability to generate realistic and expressive speech with fine-grained control over speaker identity and expression, limiting the realism and flexibility of in-game dialogue.
Innovation Solution
A computer-implemented method for training a machine-learned speech audio generation system that processes training examples comprising ground-truth acoustic features, speech content data, and speaker identifiers. The system generates speaker embeddings, expression embeddings, and speech content embeddings, which are combined and classified to update parameters and improve the disentanglement of speech audio aspects, enabling more realistic and flexible speech generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a speech audio generation system is used to generate speech audio for video games, then fine-grained control of speaker identity and expression is enabled, but the realism and robustness of the generated speech audio is insufficient
Solution Approach 1:
The system segments speech audio representation into separate embeddings: speaker embeddings for speaker identity, expression embeddings for emotional state, and content embeddings for linguistic meaning. This segmentation allows independent control of each aspect while maintaining overall realism, as each embedding is trained to capture specific characteristics without interference from other factors.
Solution Approach 2:
The system introduces an intermediary classifier that operates on the combined embeddings to provide feedback during training. This classifier mediates between the generated speech audio and the target characteristics, enabling the system to learn robust representations that maintain realism while achieving fine-grained control through iterative optimization.
2Adaptability or versatility
If multiple embeddings (speaker, expression, content) are combined to generate speech audio, then fine-grained control is achieved, but the system complexity increases
Solution Approach 1:
The system merges multiple specialized encoders (speaker encoder, expression encoder, content encoder) into a unified framework where their embeddings are combined additively. This merging approach maintains the functional benefits of separate specialized components while simplifying the overall architecture compared to using multiple independent systems, as the combined embeddings can be processed through a single generation model.
Solution Approach 2:
The combined embedding framework serves multiple functions simultaneously: it enables speaker identity control, expression control, and content generation through a single unified representation. This multi-functionality reduces system complexity by eliminating the need for separate generation pathways for each speech aspect, as all controls are integrated into one comprehensive embedding structure.
3Measurement precision
If speaker embeddings and expression embeddings are dis entangled, then control precision over speaker identity and expression is improved, but the training complexity and computational requirements increase
Solution Approach 1:
The system implements feedback through a classifier that evaluates the combined embeddings during training. This feedback mechanism guides the disentanglement process by providing gradient signals that encourage speaker embeddings to capture only speaker-specific characteristics and expression embeddings to capture only expression-specific characteristics, achieving precise control without requiring complex manual intervention or iterative manual tuning.
Data Source
AI summary
This specification describes a computer-implemented method of training a machine-learned speech audio generation system for use in video games. The training comprises: receiving one or more training examples. Each training example comprises: (i) ground-truth acoustic features for speech audio, (ii) speech content data representing speech content of the speech audio, and (iii) a ground-truth speaker identifier for a speaker of the speech audio. Parameters of the machine-learned speech audio generation system are updated to: (i) minimize a measure of difference between the predicted acoustic features of a training example and the corresponding ground-truth acoustic features of the training example, (ii) maximize a measure of difference between the first speaker classification for the training example and the corresponding ground-truth speaker identifier of the training example, and (iii) minimize a measure of difference between the second speaker classification for the training example and the corresponding ground-truth speaker identifier of the training example.