18151186. TEXT DATA PROCESSING METHOD AND APPARATUS simplified abstract (Huawei Technologies Co., Ltd.)
Contents
TEXT DATA PROCESSING METHOD AND APPARATUS
Organization Name
Inventor(s)
TEXT DATA PROCESSING METHOD AND APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18151186 titled 'TEXT DATA PROCESSING METHOD AND APPARATUS
Simplified Explanation
The patent application describes a method for processing text data using a noise generation model and a text processing model.
- The method involves obtaining a target text and processing it using a noise generation model to obtain a noisy text.
- The noise generation model is trained using training data that includes a correct text corresponding to speech data and a second text obtained through speech recognition using a speech recognition model.
- A text processing model is then trained using the noisy text as training data to obtain a trained text processing model.
Potential Applications
- Text data processing for speech recognition systems.
- Natural language processing tasks such as machine translation, sentiment analysis, and text summarization.
Problems Solved
- Improving the accuracy of text data processing by incorporating noise generation and training models.
- Addressing the challenges of processing noisy text data obtained from speech recognition systems.
Benefits
- Enhanced performance of speech recognition systems by training models using noisy text data.
- Improved accuracy and efficiency in natural language processing tasks.
- Potential for more accurate and reliable machine translation, sentiment analysis, and text summarization.
Original Abstract Submitted
This application discloses example text data processing method. One example method includes obtaining a target text. The target text can then be processed based on a noise generation model to obtain a noisy text, where when the noise generation model is trained, training data of the noise generation model at least includes a first text and a second text, the first text is a correct text corresponding to speech data, and the second text is obtained by performing speech recognition on the speech data by using a first speech recognition model. A text processing model can then be trained, by using at least the noisy text as training data, to obtain a trained text processing model.