Warped Spectral Audio Encoding for Speech Recognition and Reconstruction
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Solution Overview
Problem
Existing voice codecs and speech recognition systems have limited success when attempting to use encoded speech recognition features to construct audio signals or vice versa, resulting in reconstructed audio signals that are not close representations of the original.
Innovation Solution
The use of a warped spectral estimate of an original audio signal to encode fine features, which can be used for speech recognition and to reconstruct a reconstructed audio signal that approximates the original, involves encoding and decoding representations of warped frequency spectral estimates and fine estimates, with dynamic range reduction and expansion operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If encoded speech recognition features are used to construct audio signals, then speech recognition performance is improved, but the reconstructed audio signal quality deteriorates
Solution Approach 1:
The audio signal representation is segmented into two distinct components: warped spectral estimates (for speech recognition) and fine estimates (for audio reconstruction). This segmentation allows each component to be optimized for its specific purpose without compromise, resolving the contradiction between speech recognition performance and audio signal quality.
Solution Approach 2:
Fine estimates serve as an intermediary component that bridges the gap between warped spectral estimates and reconstructed audio signals. By introducing this intermediate representation, the system can achieve both accurate speech recognition and high-quality audio reconstruction, as the fine estimates provide the additional detail needed for faithful signal reproduction.
2Manufacturing precision
If encoded voice codec features are used for speech recognition, then audio signal quality is preserved, but speech recognition performance deteriorates
Solution Approach 1:
The system segments the feature extraction process into two parallel paths: one generating warped spectral estimates optimized for speech recognition, and another generating fine estimates optimized for audio reconstruction. This segmentation eliminates the need to compromise either speech recognition performance or audio signal quality.
Solution Approach 2:
The encoding system becomes universal by producing multiple types of representations from the same input audio signal. The warped spectral estimates serve speech recognition functions while the fine estimates serve audio reconstruction functions, allowing the system to fulfill multiple purposes simultaneously without degradation in either domain.
3Device complexity
If a single encoding scheme is used for both speech recognition and audio reconstruction, then system complexity is reduced, but both speech recognition performance and audio reconstruction quality deteriorate
Solution Approach 1:
The encoding system is segmented into two specialized sub-systems: a warped spectral estimation sub-system for speech recognition and a fine estimation sub-system for audio reconstruction. While this increases overall system complexity compared to a single scheme, it dramatically improves both speech recognition performance and audio reconstruction quality by allowing each sub-system to be optimized for its specific function.
Data Source
AI summary
A warped spectral estimate of an original audio signal can be used to encode a representation of a fine estimate of the original signal. The representation of the warped spectral estimate and the representation of the fine estimate can be sent to a speech recognition system. The representation of the warped spectral estimate can be passed to a speech recognition engine, where it may be used for speech recognition. The representation of the warped spectral estimate can also be used along with the representation of the fine estimate to reconstruct a representation of the original audio signal.