Speech Prosody Disentanglement Without Text Transcriptions
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Solution Overview
Problem
Existing prosody style transfer algorithms rely on text transcriptions, limiting their application to high-resource languages and failing to effectively disentangle pitch and rhythm components in speech, which are crucial for speaker and emotion recognition.
Innovation Solution
A machine learning model that disentangles prosody without text transcriptions using a two-stage training strategy and self-expressive representation learning, employing similarity-based random resampling to obscure rhythm and decode speech based on high-level domain summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text transcriptions are used to identify content information, then prosody style transfer can be achieved, but the application is confined to high-resource languages only
Solution Approach 1:
The patent extracts and removes the dependency on text transcriptions from the prosody style transfer system. By using self-supervised learning and acoustic feature analysis directly on speech signals, the system separates the content identification process from text-based methods, enabling application to low-resource languages without requiring transcription resources.
Solution Approach 2:
The patent introduces acoustic feature representations and self-supervised learning models as intermediaries between the speech signal and content identification. These intermediaries enable the system to understand speech content through acoustic patterns alone, bypassing the need for text transcriptions and expanding applicability to languages without extensive textual resources.
2Measurement precision
If speech information is decomposed into four components (language content, timbre, pitch, and rhythm), then disentangled representations can be obtained, but explicit annotations for each component are difficult and expensive to obtain
Solution Approach 1:
The patent implements self-supervised learning where the system learns to decompose speech into linguistic content and prosodic components without external annotations. The model serves itself by using the speech signals and their transformations to automatically learn the disentangled representations, eliminating the need for expensive manual annotation of pitch, rhythm, and other prosody components.
Solution Approach 2:
The patent performs preliminary decomposition of speech into content and prosody components during the training phase using self-supervised objectives. By pre-training the model to separate these components before actual style transfer tasks, the system establishes disentangled representations that can be reused without requiring component-specific annotations during deployment.
3Reliability
If rhythm information is preserved in the speech representation, then speech naturalness is maintained, but prosody style transfer cannot be effectively performed
Solution Approach 1:
The patent segments the speech representation into distinct linguistic content features and prosodic features (including rhythm). By separating these components in the feature space, the system can manipulate prosody for style transfer while preserving the naturalness of the speech content, allowing independent control over different speech dimensions.
Solution Approach 2:
The patent applies different processing qualities to different parts of the speech representation. Linguistic content features are preserved with high fidelity to maintain speech naturalness, while prosodic features are selectively modified to enable style transfer. This local differentiation allows simultaneous achievement of naturalness and transfer capability.
Data Source
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AI summary
A computer-implemented method is provided of using a machine learning model for disentanglement of prosody in spoken natural language. The method includes encoding, by a computing device, the spoken natural language to produce content code. The method further includes resampling, by the computing device without text transcriptions, the content code to obscure the prosody by applying an unsupervised technique to the machine learning model to generate prosody-obscured content code. The method additionally includes decoding, by the computing device, the prosody-obscured content code to synthesize speech indirectly based upon the content code.