11. A system for speech recognition, comprising: a speech recognition engine configured to generate a set of likely hypotheses using a speech recognition method for recognizing speech; a unified language model including a semantic language model and a lexical language model configured for rescoring the likely hypotheses to improve recognition results by using sentence-based semantic content and lexical content wherein the unified language model is trained by including a unigram feature, a bigram feature, a trigram feature, a current active parent label (Li), a number of tokens (Ni) to the left since current parent label (Li) starts, a previous closed constituent label (Oi), a number of tokens (Mi) to the left after the previous closed constituent label finishes, and a number of questions to classify parser tree entries, wherein the questions include a default, (wj−1), (wj−1, wj−2), (Li), (Li,Ni), (Li,Ni, wj−1), and (Oi,Mi), where w represents a word and j is and index representing word position to compute word probabilities; and the speech recognition engine configured to score parse trees to identify a best sentence according to the sentences' parse tree by employing semantic information and lexical information in the parse tree to clarify the recognized speech.
11. A system for speech recognition, comprising: a speech recognition engine configured to generate a set of likely hypotheses using a speech recognition method for recognizing speech; a unified language model including a semantic language model and a lexical language model configured for rescoring the likely hypotheses to improve recognition results by using sentence-based semantic content and lexical content wherein the unified language model is trained by including a unigram feature, a bigram feature, a trigram feature, a current active parent label (Li), a number of tokens (Ni) to the left since current parent label (Li) starts, a previous closed constituent label (Oi), a number of tokens (Mi) to the left after the previous closed constituent label finishes, and a number of questions to classify parser tree entries, wherein the questions include a default, (wj−1), (wj−1, wj−2), (Li), (Li,Ni), (Li,Ni, wj−1), and (Oi,Mi), where w represents a word and j is and index representing word position to compute word probabilities; and the speech recognition engine configured to score parse trees to identify a best sentence according to the sentences' parse tree by employing semantic information and lexical information in the parse tree to clarify the recognized speech. 18. The system as recited in claim 11 , further comprising a confidence measurement module.