| CPC G10L 15/063 (2013.01) [G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06F 40/40 (2020.01); G10L 15/16 (2013.01); G10L 15/20 (2013.01); G10L 15/22 (2013.01)] | 20 Claims |

|
1. A text data processing method, comprising:
obtaining speech data and a first text, wherein the first text is a correct text corresponding to the speech data;
performing automatic speech recognition (ASR) on the speech data based on a first speech recognition model to obtain a second text;
processing the first text based on an initial noise generation model to obtain an output text;
obtaining a loss based on the output text and the second text;
updating the initial noise generation model based on the loss until the loss meets a preset condition to obtain a noise generation model, wherein the noise generation model is at least one of the following: a bidirectional long short-term memory (LSTM), a generative pre-training (GPT) model, or a Laser Tagger model;
obtaining a target text;
processing the target text based on the noise generation model to obtain a noisy text; and
training a text processing model, by using at least the noisy text as training data, to obtain a trained text processing model, wherein the trained text processing model is used to perform at least one of the following tasks: text translation, text semantic recognition, text classification, automatic question answering, information recommendation, or text emotion recognition.
|