1. A method for speech recognition, comprising the steps of: generating a set of likely hypotheses using a speech recognition method for recognizing speech; rescoring the likely hypotheses by using sentence based semantic content and lexical content by employing a semantic structured language model which combines a semantic language model and a lexical language model wherein the semantic structured 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, wj−1), and (Oi, Mi), where w represents a word and j is and index representing word position; and scoring 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.
1. A method for speech recognition, comprising the steps of: generating a set of likely hypotheses using a speech recognition method for recognizing speech; rescoring the likely hypotheses by using sentence based semantic content and lexical content by employing a semantic structured language model which combines a semantic language model and a lexical language model wherein the semantic structured 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, wj−1), and (Oi, Mi), where w represents a word and j is and index representing word position; and scoring 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. 4. The method as recited in claim 1 , wherein the step of rescoring employs maximum entropy method 2 (MELM2) or maximum entropy method 3 (MELM3) semantic structured language models.