Kernel Deep Convex Networks for Speech Intent Recognition
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Solution Overview
Problem
Current speech and language understanding systems face challenges in accurately determining the intent and domain of spoken language, as well as extracting relevant slots or concepts, due to limitations in feature extraction and classification methods.
Innovation Solution
A system incorporating a language understanding engine with a feature acquisition component that extracts local, discriminative features using a deep convex network integrated with a kernel trick, and a slot-filling component that determines probabilities via a softmax interface, enabling end-to-end learning for improved slot-filling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature extraction and classification methods are used in speech and language understanding systems, then the system structure is simple and easy to implement, but the accuracy of intent determination and slot-filling tasks is insufficient
Solution Approach 1:
The patent combines feature extraction and classification into a unified deep convex network architecture. The kernel deep convex network integrates multiple processing stages (feature extraction, non-linear transformation, classification) into a single end-to-end trainable system, eliminating the need for separate traditional modules and improving slot-filling accuracy while managing complexity through unified architecture.
Solution Approach 2:
The patent transforms the classification approach by changing from traditional discrete classification parameters to continuous convex optimization parameters. The use of convex loss functions and gradient-based optimization enables more precise parameter tuning and improves measurement precision in slot-filling tasks compared to traditional methods.
2Measurement precision
If a deep convex network with kernel trick is used for feature extraction, then the accuracy of spoken language understanding is improved, but the computational complexity increases due to implicit computation of hidden units
Solution Approach 1:
The patent extracts and utilizes only the essential kernel function computations while omitting the explicit computation of high-dimensional hidden units. By taking out the core computational element (kernel trick) and removing the computationally expensive intermediate representations, the system achieves high accuracy without the full computational burden of traditional deep networks.
Solution Approach 2:
The kernel function serves as an intermediary that implicitly maps input features to high-dimensional space without explicitly computing the hidden unit activations. This intermediary mechanism enables the network to benefit from complex non-linear transformations while avoiding the computational cost of actually materializing the high-dimensional representations.
3Measurement precision
If explicit computation of hidden units is performed in deep networks, then the model can capture complex patterns, but overfitting occurs and parameter tuning becomes difficult
Solution Approach 1:
The patent removes the explicit hidden unit computation step that causes overfitting, retaining only the essential feature transformation capabilities through the kernel trick. This extraction of the core functionality without the problematic intermediate representations improves model generalization while maintaining pattern recognition accuracy.
Solution Approach 2:
Instead of explicitly computing hidden units and then applying classification (forward approach), the patent inverts the approach by directly computing kernel functions that implicitly represent the hidden space. This inversion eliminates the overfitting-prone explicit hidden units while preserving the ability to capture complex patterns through the dual representation of kernel methods.
Data Source
AI summary
Data associated with spoken language may be obtained. An analysis of the obtained data may be initiated for understanding of the spoken language using a deep convex network that is integrated with a kernel trick. The resulting kernel deep convex network may also be constructed by stacking one shallow kernel network over another with concatenation of the output vector of the lower network with the input data vector. A probability associated with a slot that is associated with slot-filling may be determined, based on local, discriminative features that are extracted using the kernel deep convex network.