[论文解读] ASR Context-Sensitive Error Correction Based on Microsoft N-Gram Dataset

Youssef Bassil, Paul Semaan|arXiv (Cornell University)|Mar 23, 2012

Natural Language Processing Techniques参考文献 19被引用 18

一句话总结

本文提出了一种针对自动语音识别（ASR）系统的上下文敏感错误纠正方法，利用微软N元语法数据集检测并纠正拼写错误。通过利用真实网络来源的n-gram实现稳健的词汇覆盖，该方法采用错误检测、候选生成和上下文感知选择的流程，在多种语音输入下显著降低了ASR错误率。

ABSTRACT

At the present time, computers are employed to solve complex tasks and problems ranging from simple calculations to intensive digital image processing and intricate algorithmic optimization problems to computationally-demanding weather forecasting problems. ASR short for Automatic Speech Recognition is yet another type of computational problem whose purpose is to recognize human spoken speech and convert it into text that can be processed by a computer. Despite that ASR has many versatile and pervasive real-world applications,it is still relatively erroneous and not perfectly solved as it is prone to produce spelling errors in the recognized text, especially if the ASR system is operating in a noisy environment, its vocabulary size is limited, and its input speech is of bad or low quality. This paper proposes a post-editing ASR error correction method based on MicrosoftN-Gram dataset for detecting and correcting spelling errors generated by ASR systems. The proposed method comprises an error detection algorithm for detecting word errors; a candidate corrections generation algorithm for generating correction suggestions for the detected word errors; and a context-sensitive error correction algorithm for selecting the best candidate for correction. The virtue of using the Microsoft N-Gram dataset is that it contains real-world data and word sequences extracted from the web which canmimica comprehensive dictionary of words having a large and all-inclusive vocabulary. Experiments conducted on numerous speeches, performed by different speakers, showed a remarkable reduction in ASR errors. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor and distributed systems.

研究动机与目标

• 解决ASR输出中持续存在的拼写错误，尤其是在噪声较大或语音质量较低的条件下。
• 通过使用上下文语言模型检测并纠正词级别错误，改进ASR转录的后处理。
• 利用大规模真实世界n-gram数据实现全面的词汇覆盖，提升纠正准确率。
• 开发一个三阶段纠正流水线：错误检测、候选生成和上下文敏感选择。

提出的方法

• 使用微软N元语法数据集作为真实世界词序列和频率统计的来源，以建模自然语言模式。
• 采用基于规则和统计的错误检测算法，识别ASR输出中可能的拼写错误。
• 利用n-gram上下文和原始词的语音相似性生成纠正候选。
• 应用上下文敏感的选择机制，根据n-gram概率和周围词语上下文对候选进行排序。
• 利用三元语法及更高阶n-gram评估候选纠正结果在特定句子上下文中的可能性。
• 将纠正流水线集成到ASR转录的后处理框架中。

实验结果

研究问题

• RQ1 真实世界n-gram数据能否提升ASR引发的拼写错误的检测与纠正效果？
• RQ2 上下文敏感选择在从多个候选中选出最合理纠正结果方面有多高效？
• RQ3 与较小的词典相比，使用微软N元语法数据集在词汇覆盖和纠正准确率方面提升了多少？
• RQ4 所提出的方法能否在不同说话人和语音质量条件下降低ASR错误率？
• RQ5 使用高阶n-gram对纠正的精确率和召回率有何影响？

主要发现

• 所提出的方法在来自不同说话人的多个语音样本中显著降低了ASR错误率。
• 使用微软N元语法数据实现了全面的词汇覆盖，提升了对罕见词或未登录词的检测能力。
• 上下文敏感的纠正选择优于简单的词汇匹配，通过利用句子级上下文实现了更优表现。
• 错误检测模块即使在低质量或噪声较大的语音输入中也能有效识别拼写错误。
• 该系统在不同语音质量和说话人特征下表现出强鲁棒性。
• 未来工作建议可通过并行化处理扩展该方法，以支持大规模或实时ASR纠正。