Unscented Transformation for Online Speech Distortion Estimation
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Solution Overview
Problem
Existing automatic speech recognition (ASR) technologies face limitations in accuracy due to ambient noise and channel distortions, as previous solutions like JAC-VTS models rely on shared linear mappings across the entire model space, which are nonlinear in reality.
Innovation Solution
The implementation of an unscented transformation framework for estimating noise and channel distortions, allowing for non-linear mapping of static and dynamic distorted speech parameters, enabling online estimation and updating of these parameters within the framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a shared linear mapping (VTS approximation) is used across the entire model space, then the device complexity is reduced and computation is simplified, but the measurement precision of distorted speech parameters deteriorates because the true mapping is nonlinear
Solution Approach 1:
The model space is segmented into multiple local regions, each with its own linear mapping. This allows the system to capture nonlinear relationships by composing multiple local linear approximations, thereby improving measurement precision without requiring a single complex global nonlinear model.
Solution Approach 2:
The system dynamically selects or adapts the appropriate linear mapping based on the current operating point in the model space. This dynamic adaptation allows the linear mappings to collectively approximate the nonlinear true mapping across different regions, resolving the contradiction between simplicity and accuracy.
2Reliability
If online distortion parameter estimation is implemented, then the reliability of speech recognition in noisy environments is improved, but the computation time and processing complexity increase
Solution Approach 1:
Linear mappings are pre-computed and stored for different regions of the model space before online operation. During online speech recognition, the system only needs to identify the current region and apply the corresponding pre-computed mapping, significantly reducing real-time computation time while maintaining reliable distortion parameter estimation.
Solution Approach 2:
The system uses the observed distorted speech characteristics to automatically identify the current operating region and select the appropriate pre-computed linear mapping without requiring external intervention or complex iterative optimization, thereby achieving fast online adaptation.
Data Source
AI summary
Noise and channel distortion parameters in the vectorized logarithmic or the cepstral domain for an utterance may be estimated, and subsequently the distorted speech parameters in the same domain may be updated using an unscented transformation framework during online automatic speech recognition. An utterance, including speech generated from a transmission source for delivery to a receiver, may be received by a computing device. The computing device may execute instructions for applying the unscented transformation framework to speech feature vectors, representative of the speech, in order to estimate, in a sequential or online manner, static noise and channel distortion parameters and dynamic noise distortion parameters in the unscented transformation framework. The static and dynamic parameters for the distorted speech in the utterance may then be updated from clean speech parameters and the noise and channel distortion parameters using non-linear mapping.