Robust Keyword Spotting via Sparse Subspace Alignment
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Solution Overview
Problem
Conventional speech recognition methods degrade significantly in mismatched environments due to background noise, and existing noise filtering and compensation techniques require a priori knowledge of the noise or speech environment, which is often difficult to obtain in real-world applications, especially in mobile environments with unpredictable non-stationary noise.
Innovation Solution
A computer-implemented method that extracts time-frequency speech features, aligns them, constructs a common subspace for sparse representation, and uses simultaneous sparse approximation to identify keywords in continuous speech data, employing techniques like Dynamic Time Warping and Simultaneous Orthogonal Matching Pursuit, allowing for noise-resilient keyword spotting without extensive knowledge of the noise or environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional speech recognition methods are used, then the system is simple to implement, but the recognition accuracy degrades significantly in mismatched environments with background noise
Solution Approach 1:
The speech signal is segmented into multiple time-frequency feature vectors that are processed independently through sparse approximation, allowing the system to identify keywords robustly even when parts of the signal are corrupted by noise or environmental mismatch
Solution Approach 2:
A dictionary of basis vectors serves as an intermediary representation that bridges the reference speech patterns and test speech signals. This dictionary enables the system to compare speech patterns through sparse coefficients rather than direct signal comparison, improving robustness to noise and environmental variations
2Reliability
If noise filtering and compensation techniques are used, then the recognition accuracy improves, but the system requires a priori knowledge of the noise or speech environment which is difficult to obtain
Solution Approach 1:
The system performs self-adaptation by learning the speech patterns directly from reference examples without requiring external noise models or environmental knowledge. The sparse approximation automatically adapts to the specific speech characteristics in the reference data, making the system easy to deploy in unknown environments
Solution Approach 2:
The system changes the representation parameters from raw speech signals to sparse time-frequency coefficients. This transformation allows the system to capture essential speech characteristics while being invariant to noise and environmental conditions, eliminating the need for a priori noise knowledge
Data Source
AI summary
A method for speech recognition, the method includes: extracting time-frequency speech features from a series of reference speech elements in a first series of sampling windows; aligning reference speech elements that are not of equal time span duration; constructing a common subspace for the aligned speech features; determining a first set of coefficient vectors; extracting a time-frequency feature image from a test speech stream spanned by a second sampling window; approximating the extracted image in the common subspace for the aligned extracted time-frequency speech features with a second coefficient vector; computing a similarity measure between the first and the second coefficient vector; determining if the similarity measure is below a predefined threshold; and wherein a match between the reference speech elements and a portion of the test speech stream is made in response to a similarity measure below a predefined threshold. The said reference speech elements correspond to a keyword of interest, wherein Simultaneous Orthogonal Matching Pursuit (SOMP) is used in their alignment.