  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.
