Summary of Improving Speech Recognition Error Prediction For Modern and Off-the-shelf Speech Recognizers, by Prashant Serai et al.
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers
by Prashant Serai, Peidong Wang, Eric Fosler-Lussier
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper proposes methods for modeling errors in speech recognition systems, which can be useful for tasks like discriminative language modeling and improving NLP system robustness. Previous work focused on replicating the behavior of GMM-HMM-based systems, but this study addresses the unique characteristics of modern posterior-based neural network acoustic models. The authors extend a prior phonetic confusion model by introducing a sampling-based paradigm that simulates the behavior of posterior-based models and replacing the confusion matrix with a sequence-to-sequence model to incorporate context dependency. The proposed methods are evaluated on unseen data and a novel task, showing improved predictive accuracy within a 100-guess paradigm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how speech recognition systems make mistakes. This is important because we can use this information to improve language processing systems that don’t have much audio data. Right now, most of these models are based on old technology called GMM-HMM, but newer models are different and need new approaches. The authors developed two new ways to predict errors: one uses a random sampling method to make the predictions more accurate, and the other uses a sequence-to-sequence model to consider the context of the error. They tested these methods by predicting the mistakes made by an existing speech recognition system on new data. |
Keywords
» Artificial intelligence » Confusion matrix » Neural network » Nlp » Sequence model
