Decoder Affine Conditioning for Low-Bitrate Speech Coding
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Solution Overview
Problem
State-of-the-art neural speech coding methods face inefficiencies in coding efficiency due to the entanglement of content and style information, particularly in preserving speaker identity and speaking style, which affects the quality and bitrate of reconstructed speech.
Innovation Solution
A decoder architecture that disentangles content and style information by using a conditioning entity to perform a learnable affine transform on encoded content information, combined with a decoder that separates and decodes these components, allowing for separate manipulation and transmission of long-term and short-term information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If content and style information are entangled in a single encoding stream, then the decoding process is simpler, but coding efficiency deteriorates and speaker identity preservation becomes difficult
Solution Approach 1:
The patent divides the encoded audio signal into two separate streams: content information (short-term phonetic content) and style information (long-term speaker identity and speaking style). This segmentation allows each stream to be processed independently with optimized decoding strategies, improving coding efficiency while maintaining manageable decoder complexity through structured organization.
Solution Approach 2:
The patent extracts style information as a separate encoded stream from the content information. By taking out the style component (speaker identity, speaking style) as an independent entity, the system can preserve speaker characteristics more effectively while allowing the content stream to be decoded independently, thus resolving the contradiction between simplicity and efficiency.
2Reliability
If style information is transmitted frequently to maintain speaker identity, then speaker identity preservation improves, but bitrate increases
Solution Approach 1:
The patent employs periodic transmission of style information at lower frequencies compared to content information. Since style characteristics (speaker identity, speaking style) are long-term properties that change slowly, transmitting them periodically rather than at every time step maintains reliable speaker identity preservation while significantly reducing the overall bitrate requirement.
3Device complexity
If a single decoding process is used for both content and style, then the decoding process is simpler, but the quality of reconstructed speech deteriorates
Solution Approach 1:
The patent implements a segmented decoding architecture where content information and style information are decoded through separate but coordinated processes. The content decoder handles short-term phonetic details while the style decoder (conditioning entity) handles long-term speaker characteristics. This segmentation enables each decoder to be optimized for its specific function, achieving high speech reconstruction quality without requiring an overly complex unified decoder.
4Ease of manufacture
If all information is encoded together, then encoding is simpler, but generalization ability deteriorates
Solution Approach 1:
The patent extracts style information as a separate encoded representation that can be independently manipulated and applied. This extraction enables the system to generalize speaker characteristics across different audio inputs, improving adaptability and versatility. The separated style encoding can be conditioned on different speakers and styles, allowing the system to generalize better while the encoding process remains structured and manageable.
Data Source
Figure 1~2
Figure 3a~3b
AI summary
Decoder (20') for decoding an encoded input audio signal, the decoder comprising: a conditioning entity (20c), receiving an encoded style information derived from the encoded input audio signal and performing at least a learnable affine transform of an encoded content information derived from the encoded input audio signal to obtain a transformed encoded content information; and a decoder (20d) configured to decode the transformed encoded content information further.