International business machines corporation (20240194184). TESTING CASCADED DEEP LEARNING PIPELINES COMPRISING A SPEECH-TO-TEXT MODEL AND A TEXT INTENT CLASSIFIER simplified abstract

TESTING CASCADED DEEP LEARNING PIPELINES COMPRISING A SPEECH-TO-TEXT MODEL AND A TEXT INTENT CLASSIFIER

Organization Name

international business machines corporation

Inventor(s)

Swagatam Haldar of Kolkata (IN)

Diptikalyan Saha of Bangalore (IN)

Deepak Vijaykeerthy of Bangalore (IN)

Aniya Aggarwal of New Delhi (IN)

Nishtha Madaan of Gurgaon (IN)

TESTING CASCADED DEEP LEARNING PIPELINES COMPRISING A SPEECH-TO-TEXT MODEL AND A TEXT INTENT CLASSIFIER - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240194184 titled 'TESTING CASCADED DEEP LEARNING PIPELINES COMPRISING A SPEECH-TO-TEXT MODEL AND A TEXT INTENT CLASSIFIER

Simplified Explanation

The patent application describes a system and method for testing a cascaded pipeline used in speech data processing. The system evaluates the robustness of the pipeline by comparing its output with labeled speech data.

Key Features and Innovation

• System for testing a cascaded pipeline in speech data processing.
• Evaluation of pipeline robustness by comparing output with labeled data.
• Utilizes a first and second model in the cascaded pipeline.

Potential Applications

The technology can be used in:

• Speech recognition systems.
• Natural language processing applications.
• Voice-controlled devices.

Problems Solved

• Ensures the accuracy and reliability of speech data processing.
• Helps identify and address issues in the cascaded pipeline.
• Improves the overall performance of speech recognition systems.

Benefits

• Enhanced accuracy in speech data processing.
• Increased reliability of speech recognition systems.
• Efficient identification and resolution of pipeline issues.

Commercial Applications

• "Testing System for Speech Data Processing Pipelines": Potential commercial uses in the development and testing of speech recognition software for various industries, including telecommunications, healthcare, and automotive.

Prior Art

Readers can explore prior art related to speech data processing pipelines, speech recognition systems, and machine learning models in the field of natural language processing.

Frequently Updated Research

Stay updated on advancements in speech data processing pipelines, machine learning models, and evaluation methods for speech recognition systems.

Questions about Speech Data Processing Pipelines

How does the system evaluate the robustness of the cascaded pipeline?

The system evaluates the robustness by comparing the output of the pipeline with labeled speech data to assess its accuracy and performance.

What are the potential applications of this technology beyond speech recognition systems?

This technology can also be applied in natural language processing applications, voice-controlled devices, and other speech data processing systems to enhance their performance and reliability.

Original Abstract Submitted

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.