1. A computer implemented method for expanding a language model corresponding to a domain, comprising: collecting, by one or more processor, at least one feature vector from one or more external domain distinctive from the domain, wherein the domain and the one or more external domain are interconnected via a cloud; expanding the language model, stored in a corpora coupled to the cloud, with a feature vector of the at least one feature vector from the collecting, wherein the feature vector is, based on a logistic regression threshold, more relevant to the domain than not, the expanding comprising: (i) calculating a logistic regression value for the feature vector by use of σ(t)=e t /(e t +1)=1/(1+e −t ), wherein σ(t) indicates a standard equation of logistic regression for sentence t that represents the feature vector, (ii) ascertaining that the logistic regression value for the feature vector indicating that the probability of the feature vector to be relevant to the domain is greater than or equal to the logistic regression threshold of zero point five (0.5); and (iii) updating the language model in the corpora by adding the feature vector from the ascertaining to the language model; enhancing the language model by machine learning live content from one or more subject website in which the domain is interested such that the language model includes the live content and one or more secondary term derived from the live content that are more relevant to the domain than not, 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.
5. The computer implemented method of claim 1 , further comprising: evaluating the language model from the expanding and from the enhancing, by use of evaluation techniques selected from Word Error Rate (WER), a ground truth test, and combinations thereof.