Abstract: Text output of speech recognition engines tend to be erroneous when spoken data has domain specific terms. The present disclosure facilitates automatic correction of errors in speech to text conversion using abstractions of evolutionary development and artificial development. The words in a speech recognition engine text output are treated as a set of injured genes in a biological cell that need repair which are then repaired and form genotypes that are then repaired to phenotypes through a series of repair steps based on a matching, mapping and linguistic repair through a fitness criteria. A basic genetic level repair involves phonetic MATCHING function together with a FITNESS function to select the best among the matching genes. A second genetic level repair involves a contextual MAPPING function for repairing remaining ‘injured’ genes of the speech recognition engine output. Finally, a genotype to phenotype repair involves using linguistic rules and semantic rules of the domain.

Abstract: Text output of speech recognition engines tend to be erroneous when spoken data has domain specific terms. The present disclosure facilitates automatic correction of errors in speech to text conversion using abstractions of evolutionary development and artificial development. The words in a speech recognition engine text output are treated as a set of injured genes in a biological cell that need repair which are then repaired and form genotypes that are then repaired to phenotypes through a series of repair steps based on a matching, mapping and linguistic repair through a fitness criteria. A basic genetic level repair involves phonetic MATCHING function together with a FITNESS function to select the best among the matching genes. A second genetic level repair involves a contextual MAPPING function for repairing remaining ‘injured’ genes of the speech recognition engine output. Finally, a genotype to phenotype repair involves using linguistic rules and semantic rules of the domain.