Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism

• URL: http://arxiv.org/abs/2308.03423v1
• Date: Mon, 7 Aug 2023 09:19:59 GMT
• Title: Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism
• Authors: Jiaxin Fan, Yong Zhang, Hanzhang Li, Jianzong Wang, Zhitao Li, Sheng Ouyang, Ning Cheng, Jing Xiao
• Abstract summary: Current mainstream models often struggle with effectively utilizing word-level features and phonetic information. This paper introduces a novel approach that incorporates a dynamic error scaling mechanism to detect and correct phonetically erroneous text.
• Score: 27.09416337926635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chinese Automatic Speech Recognition (ASR) error correction presents significant challenges due to the Chinese language's unique features, including a large character set and borderless, morpheme-based structure. Current mainstream models often struggle with effectively utilizing word-level features and phonetic information. This paper introduces a novel approach that incorporates a dynamic error scaling mechanism to detect and correct phonetically erroneous text generated by ASR output. This mechanism operates by dynamically fusing word-level features and phonetic information, thereby enriching the model with additional semantic data. Furthermore, our method implements unique error reduction and amplification strategies to address the issues of matching wrong words caused by incorrect characters. Experimental results indicate substantial improvements in ASR error correction, demonstrating the effectiveness of our proposed method and yielding promising results on established datasets.

Related papers

• Understanding and Mitigating Classification Errors Through Interpretable Token Patterns [58.91023283103762]
  Characterizing errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors. We propose to discover those patterns of tokens that distinguish correct and erroneous predictions. We show that our method, Premise, performs well in practice.
  arXiv  Detail & Related papers  (2023-11-18T00:24:26Z)
• Generative error correction for code-switching speech recognition using large language models [49.06203730433107]
  Code-switching (CS) speech refers to the phenomenon of mixing two or more languages within the same sentence. We propose to leverage large language models (LLMs) and lists of hypotheses generated by an ASR to address the CS problem.
  arXiv  Detail & Related papers  (2023-10-17T14:49:48Z)
• ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correction [30.486396813844195]
  We present a novel ASR postprocessing method that focuses on improving the recognition of rare words through error detection and context-aware error correction. Experimental results across five datasets demonstrate that our proposed method achieves significantly lower word error rates (WERs) than previous approaches.
  arXiv  Detail & Related papers  (2023-10-08T11:40:30Z)
• HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models [81.56455625624041]
  We introduce the first open-source benchmark to utilize external large language models (LLMs) for ASR error correction. The proposed benchmark contains a novel dataset, HyPoradise (HP), encompassing more than 334,000 pairs of N-best hypotheses. LLMs with reasonable prompt and its generative capability can even correct those tokens that are missing in N-best list.
  arXiv  Detail & Related papers  (2023-09-27T14:44:10Z)
• Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition [52.55136323341319]
  Existing Chinese text error detection mainly focuses on spelling and simple grammatical errors. Chinese semantic errors are understudied and more complex that humans cannot easily recognize.
  arXiv  Detail & Related papers  (2022-04-15T13:55:32Z)
• Error Correction in ASR using Sequence-to-Sequence Models [32.41875780785648]
  Post-editing in Automatic Speech Recognition entails automatically correcting common and systematic errors produced by the ASR system. We propose to use a powerful pre-trained sequence-to-sequence model, BART, to serve as a denoising model. Experimental results on accented speech data demonstrate that our strategy effectively rectifies a significant number of ASR errors.
  arXiv  Detail & Related papers  (2022-02-02T17:32:59Z)
• A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems [42.05399301143457]
  We introduce a light-weight contextual spelling correction model to correct context-related recognition errors. Experiments show that the model improves baseline ASR model performance with about 50% relative word error rate reduction. The model also shows excellent performance for out-of-vocabulary terms not seen during training.
  arXiv  Detail & Related papers  (2021-08-17T08:14:37Z)
• FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition [90.34177266618143]
  We propose FastCorrect, a novel NAR error correction model based on edit alignment. FastCorrect speeds up the inference by 6-9 times and maintains the accuracy (8-14% WER reduction) compared with the autoregressive correction model. It outperforms the accuracy of popular NAR models adopted in neural machine translation by a large margin.
  arXiv  Detail & Related papers  (2021-05-09T05:35:36Z)
• An Approach to Improve Robustness of NLP Systems against ASR Errors [39.57253455717825]
  Speech-enabled systems typically first convert audio to text through an automatic speech recognition model and then feed the text to downstream natural language processing modules. The errors of the ASR system can seriously downgrade the performance of the NLP modules. Previous work has shown it is effective to employ data augmentation methods to solve this problem by injecting ASR noise during the training process.
  arXiv  Detail & Related papers  (2021-03-25T05:15:43Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.