Summary of Text Injection For Neural Contextual Biasing, by Zhong Meng et al.
Text Injection for Neural Contextual Biasing
by Zhong Meng, Zelin Wu, Rohit Prabhavalkar, Cal Peyser, Weiran Wang, Nanxin Chen, Tara N. Sainath, Bhuvana Ramabhadran
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed contextual text injection (CTI) method enhances automatic speech recognition (ASR) by leveraging not only paired speech-text data but also a larger corpus of unpaired text to optimize the ASR model and its biasing component. This approach leverages neural contextual biasing, which improves ASR for crucial phrases within a speaker’s context. The CTI-MWER training minimizes the expected word error rate (WER) caused by contextual biasing when unpaired text is injected into the model. Experimental results show that CTI with 100 billion text sentences achieves up to 43.3% relative WER reduction from a strong neural biasing model, and further improves by 23.5% using CTI-MWER. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve speech recognition machines. It’s like when you’re having a conversation and the machine can understand what you’re saying better because it knows more about the context. The researchers use a special technique called contextual text injection to make this happen. They combine two types of data: things people say (speech) and written words (text). This helps the machine learn to focus on important parts of conversations that might not be in its training data. The results are impressive, with the new method reducing mistakes by up to 43% compared to other methods. |
