18488578. Knowledge Distillation with Domain Mismatch For Speech Recognition simplified abstract (GOOGLE LLC)

Knowledge Distillation with Domain Mismatch For Speech Recognition

Organization Name

GOOGLE LLC

Inventor(s)

Tien-Ju Yang of Mountain View CA (US)

You-Chi Cheng of Mountain View CA (US)

Shankar Kumar of New York NY (US)

Jared Lichtarge of Mountain View CA (US)

Ehsan Amid of Mountain View CA (US)

Yuxin Ding of Mountain View CA (US)

Rajiv Mathews of Sunnyvale CA (US)

Mingqing Chen of Saratoga CA (US)

Knowledge Distillation with Domain Mismatch For Speech Recognition - A simplified explanation of the abstract

This abstract first appeared for US patent application 18488578 titled 'Knowledge Distillation with Domain Mismatch For Speech Recognition

Simplified Explanation

The method described in the abstract involves distilling a student ASR model from a teacher ASR model by generating augmented out-of-domain training utterances and corresponding pseudo-labels. This process helps improve the performance of the student ASR model by leveraging the knowledge of the teacher ASR model.

• Receiving distillation data with out-of-domain training utterances
• Generating augmented out-of-domain training utterances for each particular utterance
• Generating pseudo-labels using a teacher ASR model trained on target domain data
• Distilling a student ASR model from the teacher ASR model by training it with augmented utterances and pseudo-labels

Potential Applications

This technology could be applied in speech recognition systems to enhance the performance of ASR models, especially in out-of-domain scenarios.

Problems Solved

1. Improving the accuracy of ASR models in out-of-domain contexts 2. Enhancing the transfer of knowledge from a teacher ASR model to a student ASR model

Benefits

1. Increased accuracy and robustness of ASR models 2. Efficient knowledge transfer between models 3. Enhanced performance in diverse speech recognition tasks

Potential Commercial Applications

Optimizing speech recognition systems for various industries such as customer service, healthcare, and automotive sectors.

Possible Prior Art

One potential prior art could be the use of transfer learning techniques in machine learning to improve model performance in different domains.

How does this method compare to traditional training methods for ASR models?

The article does not provide a direct comparison between this method and traditional training methods for ASR models. It would be interesting to know the performance differences and efficiency gains of this distillation approach compared to conventional training techniques.

What are the limitations of this distillation process in improving ASR model performance?

The article does not discuss any potential limitations or challenges that may arise when using this distillation process. Understanding the drawbacks or constraints of this method could provide a more comprehensive view of its applicability and effectiveness in real-world scenarios.

Original Abstract Submitted

A method includes receiving distillation data including a plurality of out-of-domain training utterances. For each particular out-of-domain training utterance of the distillation data, the method includes generating a corresponding augmented out-of-domain training utterance, and generating, using a teacher ASR model trained on training data corresponding to a target domain, a pseudo-label corresponding to the corresponding augmented out-of-domain training utterance. The method also includes distilling a student ASR model from the teacher ASR model by training the student ASR model using the corresponding augmented out-of-domain training utterances paired with the corresponding pseudo-labels generated by the teacher ASR model.