Cascaded Speech Pipeline Robustness Testing Under Noisy Inputs
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Solution Overview
Problem
Existing deep learning and machine learning models, particularly cascaded pipelines like Speech-to-text and Text Intent Classifiers, lack robustness in handling perturbations, leading to incorrect outcomes when noise is introduced, and there is a lack of effective methods to test their robustness before deployment.
Innovation Solution
A system and method for testing cascaded deep learning pipelines by generating perturbed waveforms, adding noise to input data, and comparing the output with original labels to evaluate the robustness of the pipeline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models are deployed to handle real-world speech data, then they can perform speech-to-text conversion and text intent classification, but they lack robustness when noise or perturbations are introduced to the input
Solution Approach 1:
The patent applies preliminary action by generating perturbed test cases before deploying the model to production. The system creates noisy versions of speech inputs in advance, feeds them through the cascaded pipeline, and evaluates robustness before actual deployment. This allows identifying failure points and improving model resilience proactively rather than reactively.
Solution Approach 2:
The patent implements feedback by comparing the output of the cascaded pipeline against ground truth labels for perturbed inputs. The evaluation component analyzes whether the pipeline maintains correct classification despite noise, providing feedback on robustness performance. This feedback loop enables iterative improvement of the pipeline's resilience to perturbations.
2Measurement precision
If traditional testing methods are used to evaluate deep learning models, then individual model performance can be assessed, but the cascaded pipeline's overall robustness cannot be properly evaluated
Solution Approach 1:
The patent merges the evaluation of multiple individual models into a unified cascaded pipeline assessment. Instead of testing Speech-to-Text and Text Intent Classification models separately, the system feeds perturbed speech inputs through the entire cascaded pipeline and evaluates the combined output against ground truth. This holistic approach captures interaction effects between models that individual testing would miss.
Solution Approach 2:
The patent creates a universal evaluation framework that can assess different types of deep learning models and cascaded pipelines using the same perturbation and evaluation methodology. The system generates perturbed test cases and evaluates robustness across various model architectures and configurations, providing a versatile testing approach applicable to diverse AI systems.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate testing a cascaded pipeline. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an input component, a cascaded pipeline, and an evaluation component. The input component can receive a test case associated with a label from labeled speech data represented by waveform. The evaluation component can feed the test case to the cascaded pipeline to obtain an output of the cascaded pipeline. The evaluation component can evaluate a robustness of the cascaded pipeline by comparing the output of the cascaded pipeline and the label. The cascaded pipeline can include a first model and a second model, and the first model can be different than the second model.