Post-Editing Error Correction Algorithm for Speech Recognition using Bing Spelling Suggestion
This addresses ASR error correction for applications in noisy environments, but it is incremental as it adapts an existing spelling tool.
The paper tackled the problem of errors in automatic speech recognition (ASR) output by proposing a post-editing algorithm that uses Bing's spelling suggestion to detect and correct misrecognized words, resulting in a decrease in ASR errors and an improvement in the error correction rate.
ASR short for Automatic Speech Recognition is the process of converting a spoken speech into text that can be manipulated by a computer. Although ASR has several applications, it is still erroneous and imprecise especially if used in a harsh surrounding wherein the input speech is of low quality. This paper proposes a post-editing ASR error correction method and algorithm based on Bing's online spelling suggestion. In this approach, the ASR recognized output text is spell-checked using Bing's spelling suggestion technology to detect and correct misrecognized words. More specifically, the proposed algorithm breaks down the ASR output text into several word-tokens that are submitted as search queries to Bing search engine. A returned spelling suggestion implies that a query is misspelled; and thus it is replaced by the suggested correction; otherwise, no correction is performed and the algorithm continues with the next token until all tokens get validated. Experiments carried out on various speeches in different languages indicated a successful decrease in the number of ASR errors and an improvement in the overall error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor computers.