The Research About Keyword Spotting Of Garbage Model Based Syllable Lattice

As one special field in speech recognition research, keyword spotting is to determine occurrences of one or more keywords embedded in unconstrained extraneous speech and/or noise. It has bright future in many application areas and is an important direction. Many scholars have a study for it and bring in product.In this paper, we give a brief history keyword spotting research and provide a discussion of its fundamental principle in which two problems in this field are pointed out, that is speech signal feature and model. For speech recognition system, model is a important problem, as an feature vector reflexion a word in the lexicon, demarcate the different recognition unit with a good method. This paper describes the basic theory of HMM and presents simple and practical methods for building HMM, and build an one-phase detection-verification system. An alignment score about input speech and reference model can be obtained in the searching. That can detect keywords from continuous speech without grammar restriction.For a keyword spotting system, more based a hidden markov model, but the score and spotting method are different, building a grammar network including keyword model and garbage model representing non-keyword and background in common. A garbage model based syllable lattice is expounded in the paper for small wordage keyword spotting system, by low detection time and high diction rate.