Google llc (20240135918). Knowledge Distillation with Domain Mismatch For Speech Recognition simplified abstract

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 20240135918 titled 'Knowledge Distillation with Domain Mismatch For Speech Recognition

Simplified Explanation

The method described in the abstract involves distilling a student automatic speech recognition (ASR) model from a teacher ASR model by using out-of-domain training utterances and pseudo-labels generated by the teacher model. This process aims to improve the performance of the student model in recognizing speech in a target domain.

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

Potential Applications

This technology could be applied in various industries such as speech recognition software development, virtual assistants, customer service automation, and language translation services.

Problems Solved

1. Improving the performance of student ASR models in recognizing speech in a target domain. 2. Enhancing the accuracy and efficiency of speech recognition systems.

Benefits

1. Enhanced speech recognition capabilities in specific domains. 2. Streamlined training process for ASR models. 3. Increased accuracy and reliability of speech-to-text conversion.

Potential Commercial Applications

Optimizing Speech Recognition Models for Target Domains

Possible Prior Art

One possible prior art could be the use of transfer learning techniques in training ASR models to improve performance in specific domains.

Unanswered Questions

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

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

What are the limitations of using out-of-domain training utterances in distilling ASR models?

The abstract does not address the potential limitations or challenges associated with using out-of-domain training utterances in the distillation process. Understanding these limitations could provide insights into the applicability of this method 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.