language-model

A k skipgram is an ngram which is a superset of all ngrams and each (k-i )skipgram till (k-i)==0 (which includes 0 skip grams). So how to efficiently compute these skipgrams in python? Following is the code i tried but it is not doing as expected: <pre> input_list = ['all', 'this', 'happened', 'more', 'or', 'less'] def find_skipgrams(input_list, N,K): bigram_list = [] nlist=[] K=1 for k in range(K+1): for i in range(len(input_list)-1): if i+k+1<len(input_list): nlist=[] for j in range(N+1): if i+k+j+1<len(input_list): nlist.append(input_list[i+k+j+1]) bigram_list.append(nlist) return bigram

I am doing some development on speech to text and text to speech and I found the OpenEars API very useful. The principle of this cmu-slm based API is it uses a language model to map the speech listened by the iPhone device. So I decided to find a big English language model to feed the API speech recognizer engine. But I failed to understand the format of the voxfourge english data model to use with OpenEars. Do anyone have any idea that how can I get the .languagemodel and .dic file for English language to work with OpenEars? Halle Old question, but maybe the answer is still interesting.