8. A computer program product comprising: a non-transitory computer readable storage medium readable by one or more processor and storing instructions for execution by the one or more processor for performing a method for expanding a language model corresponding to a domain, comprising: determining that one or more word of a feature vector more supports than negates a language model corresponding to the domain based on a sensitivity of respective word, the determining comprising: (i) calculating an individual confidence score for a dictionary definition corresponding to each word of the feature vector, (ii) calculating a collective confidence score based on the individual confidence score for each word and a respective half-decay; (iii) calculating a respective sensitivity of each word as a weighted measure representing how each word supports or negates the language model, based on the collective confidence score; (iv) ascertaining that the sensitivity of one of each word is greater than or equal to a sensitivity threshold; and (v) updating the language model in the corpora by adding the one of each word from the ascertaining to the language model; adding the one or more word to the language model, wherein the language model is stored in a corpora coupled to a cloud; enhancing the language model by machine learning such that the language model accurately and comprehensively facilitates an automatic speech recognition (ASR) system for the domain; and performing speech recognition on a received speech input utilizing at least the enhanced language model.
8. A computer program product comprising: a non-transitory computer readable storage medium readable by one or more processor and storing instructions for execution by the one or more processor for performing a method for expanding a language model corresponding to a domain, comprising: determining that one or more word of a feature vector more supports than negates a language model corresponding to the domain based on a sensitivity of respective word, the determining comprising: (i) calculating an individual confidence score for a dictionary definition corresponding to each word of the feature vector, (ii) calculating a collective confidence score based on the individual confidence score for each word and a respective half-decay; (iii) calculating a respective sensitivity of each word as a weighted measure representing how each word supports or negates the language model, based on the collective confidence score; (iv) ascertaining that the sensitivity of one of each word is greater than or equal to a sensitivity threshold; and (v) updating the language model in the corpora by adding the one of each word from the ascertaining to the language model; adding the one or more word to the language model, wherein the language model is stored in a corpora coupled to a cloud; enhancing the language model by machine learning such that the language model accurately and comprehensively facilitates an automatic speech recognition (ASR) system for the domain; and performing speech recognition on a received speech input utilizing at least the enhanced language model. 9. The computer program product of claim 8 , further comprising: acquiring the feature vector from one or more external domain distinctive from the domain, live content from one or more subject website in which the domain is interested, or combinations thereof, wherein the domain, the one or more external domain, and the one or more subject website are interconnected via the cloud.