Acoustic Feature Generation With Local Timbre Embedding
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Solution Overview
Problem
Conventional acoustic models face issues such as poor sound quality, insufficient similarity between generated audio and prompt speech, and inaccurate pronunciation in timbre customization, along with information loss due to multi-stage processing in audio generation.
Innovation Solution
A method that directly generates acoustic features by combining text embedding and local timbre embedding using a self-attention-based diffusion model, bypassing semantic information and multi-stage processing, to improve timbre and pronunciation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If multi-stage processing is used in audio generation, then the generation process can be structured and controlled, but information loss occurs and processing efficiency decreases
Solution Approach 1:
The patent merges multiple processing stages into a single unified acoustic feature generation model. Instead of separate text processing, semantic analysis, and acoustic generation stages, the model directly generates acoustic features from text input, eliminating intermediate processing steps that cause information loss while maintaining structured control through the unified model architecture.
Solution Approach 2:
The patent extracts and removes the semantic information processing stage from the traditional multi-stage pipeline. By directly mapping text to acoustic features without intermediate semantic representation, the model eliminates the information loss that occurs during semantic encoding and decoding while preserving the essential text-to-speech generation capability.
2Adaptability or versatility
If conventional acoustic models are used for timbre customization, then the system can process various sound signals, but the similarity between generated audio and prompt speech is insufficient
Solution Approach 1:
The patent applies local quality by incorporating a local timbre embedding module that specifically processes timbre-related features from prompt speech. This module focuses on capturing local timbre characteristics and injecting them into the acoustic feature generation process, thereby improving timbre similarity without compromising the model's ability to process various sound signals.
Solution Approach 2:
The patent performs preliminary extraction of timbre features from prompt speech before the main acoustic feature generation process. By pre-processing the prompt speech to extract and embed timbre characteristics, the model prepares the timbre information in advance, ensuring that the generated speech accurately matches the desired timbre while maintaining versatility in processing different sound signals.
3Adaptability or versatility
If conventional acoustic models are used for pronunciation, then the system can handle various speech tasks, but pronunciation accuracy is insufficient
Solution Approach 1:
The patent applies local quality by introducing a local pronunciation embedding module that specifically processes pronunciation-related features from prompt speech. This module focuses on capturing local pronunciation characteristics and injecting them into the acoustic feature generation, thereby improving pronunciation accuracy while maintaining the model's versatility in handling various speech tasks.
Solution Approach 2:
The patent performs preliminary extraction of pronunciation features from prompt speech before the main acoustic feature generation. By pre-processing the prompt speech to extract and embed pronunciation characteristics, the model prepares the pronunciation information in advance, ensuring accurate pronunciation in the generated speech while preserving adaptability across different speech tasks.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, an apparatus, a device, a medium and a program product for generating acoustic features. The method comprises: acquiring a target text to be processed and a speech prompt having a target timbre. The method further comprises determining a text embedding based on the target text and a prompt text corresponding to the speech prompt. The method further comprises determining, based on prompt acoustic features corresponding to the speech prompt, a local timbre embedding corresponding to a plurality of feature frames of the prompt acoustic features. The method further comprises generating target acoustic features having the target timbre and corresponding to the target text based on the text embedding and the local timbre embedding.