9. A system for performing speech to text conversion, the system comprising: at least one processor; and a computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving an audio data and a video data of a user while the user is speaking; generating a first raw text based on the audio data using a language model and an acoustic model in conjunction with a Hidden Markov Model; generating a second raw text based on the video data using Karhunen-Loeve Transform (KLT) in conjunction with the Hidden Markov Model; determining a plurality of errors by comparing the first raw text and the second raw text, wherein determining the plurality of errors comprises comparing a sequence of phonemes in the first raw text with a corresponding sequence of visemes in the second raw text for one or more mismatches; correcting the plurality of errors by applying one or more rules, wherein the one or more rules employ at least one of a domain specific word database, a context of conversation, and a prior communication history; generating a correction to an error of the plurality of errors; automatically generating a rule based on the error, the correction and training; and applying the one or more rules to another error of the plurality of errors to obtain a final text.
9. A system for performing speech to text conversion, the system comprising: at least one processor; and a computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving an audio data and a video data of a user while the user is speaking; generating a first raw text based on the audio data using a language model and an acoustic model in conjunction with a Hidden Markov Model; generating a second raw text based on the video data using Karhunen-Loeve Transform (KLT) in conjunction with the Hidden Markov Model; determining a plurality of errors by comparing the first raw text and the second raw text, wherein determining the plurality of errors comprises comparing a sequence of phonemes in the first raw text with a corresponding sequence of visemes in the second raw text for one or more mismatches; correcting the plurality of errors by applying one or more rules, wherein the one or more rules employ at least one of a domain specific word database, a context of conversation, and a prior communication history; generating a correction to an error of the plurality of errors; automatically generating a rule based on the error, the correction and training; and applying the one or more rules to another error of the plurality of errors to obtain a final text. 16. The system of claim 9 , wherein the operations further comprise generating a final text based on the first raw text or the second raw text, and the correction.