18078782. FEDERATED KNOWLEDGE DISTILLATION ON AN ENCODER OF A GLOBAL ASR MODEL AND/OR AN ENCODER OF A CLIENT ASR MODEL simplified abstract (Google LLC)
FEDERATED KNOWLEDGE DISTILLATION ON AN ENCODER OF A GLOBAL ASR MODEL AND/OR AN ENCODER OF A CLIENT ASR MODEL
Organization Name
Inventor(s)
Ehsan Amid of Mountain View CA (US)
Rajiv Mathews of Sunnyvale CA (US)
Shankar Kumar of New York NY (US)
Jared Lichtarge of Brooklyn NY (US)
Mingqing Chen of Saratoga CA (US)
Tien-Ju Yang of Mountain View CA (US)
Yuxin Ding of San Francisco CA (US)
FEDERATED KNOWLEDGE DISTILLATION ON AN ENCODER OF A GLOBAL ASR MODEL AND/OR AN ENCODER OF A CLIENT ASR MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18078782 titled 'FEDERATED KNOWLEDGE DISTILLATION ON AN ENCODER OF A GLOBAL ASR MODEL AND/OR AN ENCODER OF A CLIENT ASR MODEL
Abstract: Information can be distilled from a global automatic speech recognition (ASR) model to a client ASR model. Many implementations include using an RNN-T model as the ASR model, where the global ASR model includes a global encoder, a joint network, a prediction network, and where the client ASR model includes a client encoder, the joint network, and the prediction network. Various implementations include using principal component analysis (PCA) while training the global ASR model to learn a mean vector and a set of principal components corresponding to the global ASR model. Additional or alternative implementations include training the client ASR model to generate one or more predicted coefficients of the global ASR model.
Key Features and Innovation:
- Distillation of information from a global ASR model to a client ASR model.
- Use of an RNN-T model in both the global and client ASR models.
- Incorporation of a global encoder, joint network, and prediction network in the global ASR model.
- Inclusion of a client encoder, joint network, and prediction network in the client ASR model.
- Utilization of principal component analysis (PCA) to train the global ASR model.
- Training the client ASR model to generate predicted coefficients of the global ASR model.
Potential Applications:
- Speech recognition systems
- Language translation applications
- Voice-controlled devices
- Transcription services
Problems Solved:
- Efficient transfer of information from a global ASR model to a client ASR model
- Optimization of ASR model training using PCA
- Improved accuracy and performance of client ASR models
Benefits:
- Enhanced speech recognition capabilities
- Streamlined model training process
- Increased accuracy in transcribing speech
Commercial Applications: Automatic Speech Recognition (ASR) technology for industries such as:
- Customer service
- Healthcare
- Education
- Legal transcription
Prior Art: Readers can explore prior research on ASR model distillation, RNN-T models, and PCA in the field of speech recognition technology.
Frequently Updated Research: Stay informed about the latest advancements in ASR model distillation, RNN-T models, and PCA techniques for speech recognition technology.
Questions about ASR Model Distillation: 1. How does distilling information from a global ASR model to a client ASR model improve efficiency? 2. What are the key components of an RNN-T model in the context of ASR technology?
Original Abstract Submitted
Information can be distilled from a global automatic speech recognition (ASR) model to a client ASR model. Many implementations include using an RNN-T model as the ASR model, where the global ASR model includes a global encoder, a joint network, a prediction network, and where the client ASR model includes a client encoder, the joint network, and the prediction network. Various implementations include using principal component analysis (PCA) while training the global ASR model to learn a mean vector and a set of principal components corresponding to the global ASR model. Additional or alternative implementations include training the client ASR model to generate one or more predicted coefficients of the global ASR model.
