Speech Audio Generation with Expression Embeddings
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Solution Overview
Problem
Current video game technologies lack a robust system for generating realistic speech audio with fine-grained control over speech expression, which is essential for immersive gaming experiences.
Innovation Solution
A computer-implemented method and system for training a machine-learned speech audio generation system that processes speech content and expression data to generate predicted acoustic features, utilizing an expression encoder, a synthesizer, and a prosody feature extractor to ensure accurate prosodic features and expression embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a machine-learned speech audio generation system is implemented, then realistic speech audio with fine-grained expression control is achieved, but system complexity and training requirements increase
Solution Approach 1:
The system is divided into distinct functional modules: expression encoder for processing expression data, speech content encoder for processing text content, decoder for generating acoustic features, and prosody feature extractor for deriving prosodic characteristics. Each module has a specific function, allowing the complex speech generation task to be broken down into manageable components that can be trained and optimized independently.
Solution Approach 2:
Embeddings serve as intermediary representations between the input data (expression data and speech content) and the output acoustic features. The expression encoder generates expression embeddings that capture paralinguistic characteristics, while the speech content encoder generates content embeddings from text. These embeddings are combined and decoded into acoustic features, with prosody embeddings acting as an intermediate representation of prosodic characteristics throughout the generation process.
2Reliability
If fine-grained control over speech expression is implemented, then speech realism is improved, but the amount of training data and processing required increases
Solution Approach 1:
The system controls speech expression by manipulating multiple independent parameters represented as embeddings: expression embeddings that encode paralinguistic characteristics (such as emotion, intensity, and speaking style), prosody embeddings that capture rhythmic and tonal characteristics, and content embeddings that represent the semantic meaning. By adjusting these embedding parameters, the system can generate speech with precise control over various expression dimensions without requiring exhaustive training data for every possible expression variation.
Solution Approach 2:
The system transforms the speech generation problem from a direct text-to-audio mapping into a multi-dimensional embedding space. Expression data, speech content, and prosodic features are all projected into embedding vectors that capture their essential characteristics in a compressed, continuous representation. This dimensional transformation allows the model to learn complex expression patterns from relatively limited training data by leveraging the geometric structure of the embedding space.
3Measurement precision
If multiple embedding types (expression, content, prosody) are combined, then speech audio fidelity is improved, but computational processing requirements increase
Solution Approach 1:
The system merges multiple embedding types (expression embeddings, content embeddings, and prosody embeddings) into a unified combined embedding representation that guides the acoustic feature generation process. This combined embedding captures the interaction between expression characteristics, semantic content, and prosodic features, enabling the decoder to generate acoustically faithful speech that integrates all these dimensions coherently rather than treating them as separate, independent factors.
Data Source
AI summary
This specification describes a computer-implemented method of training a machine-learned speech audio generation system to generate predicted acoustic features for generated speech audio for use in a video game. 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) speech expression data representing speech expression of the speech audio. Parameters of the machine-learned speech audio generation system are updated by: (i) minimizing a measure of difference between the predicted acoustic features for a training example and the corresponding ground-truth acoustic features of the training example, and (ii) minimizing a measure of difference between the predicted prosodic features for the training example and the corresponding ground-truth prosodic features for the training example.