Google llc (20240185841). PARAMETER-EFFICIENT MODEL REPROGRAMMING FOR CROSS-LINGUAL SPEECH RECOGNITION simplified abstract
Contents
PARAMETER-EFFICIENT MODEL REPROGRAMMING FOR CROSS-LINGUAL SPEECH RECOGNITION
Organization Name
Inventor(s)
Yu Zhang of Mountain View CA (US)
Nanxin Chen of Mountain View CA (US)
Rohit Prakash Prabhavalkar of Palo Alto CA (US)
Chao-Han Huck Yang of Mountain View CA (US)
Tara N. Sainath of Jersey City NJ (US)
Trevor Strohman of Mountain View CA (US)
PARAMETER-EFFICIENT MODEL REPROGRAMMING FOR CROSS-LINGUAL SPEECH RECOGNITION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185841 titled 'PARAMETER-EFFICIENT MODEL REPROGRAMMING FOR CROSS-LINGUAL SPEECH RECOGNITION
Simplified Explanation
The method described in the abstract involves adapting an Automatic Speech Recognition (ASR) model trained in one language to recognize speech in a second language by integrating it with input and latent reprogramming modules.
- Obtaining an ASR model trained in a first language
- Receiving transcribed training utterances in a second language
- Integrating the ASR model with an input reprogramming module and a latent reprogramming module
- Adapting the ASR model to learn how to recognize speech in the second language by training the reprogramming modules while keeping the ASR model parameters frozen
Potential Applications: This technology could be applied in language learning tools, multilingual transcription services, and voice-controlled devices that need to understand multiple languages.
Problems Solved: This technology addresses the challenge of adapting ASR models to new languages without retraining the entire model from scratch, saving time and resources.
Benefits: The technology allows for efficient adaptation of ASR models to new languages, improving the accuracy of speech recognition in multilingual environments.
Potential Commercial Applications: Commercial applications include multilingual customer service platforms, language translation services, and voice-activated devices that support multiple languages.
Possible Prior Art: One possible prior art could be the use of transfer learning techniques in machine learning to adapt models to new languages without extensive retraining.
Unanswered Questions:
1. How does the performance of the adapted ASR model compare to a model trained directly in the second language? 2. Are there any limitations to the adaptability of the ASR model to languages with significantly different phonetic structures?
Frequently Updated Research: There may be ongoing research on improving the efficiency and accuracy of adapting ASR models to new languages using reprogramming modules.
Original Abstract Submitted
a method includes obtaining an asr model trained to recognize speech in a first language and receiving transcribed training utterances in a second language. the method also includes integrating the asr model with an input reprogramming module and a latent reprogramming module. the method also includes adapting the asr model to learn how to recognize speech in the second language by training the input reprogramming module and the latent reprogramming module while parameters of the asr model are frozen.
