From HMMs to segment models: a unified view of stochastic modeling for speech recognition

@misc{citeulike:1243548, abstract = {In recent years, many alternative models have been proposed to address some of the shortcomings of the hidden Markov model, currently the most popular approach to speech recognition. In particular, a variety of models that could be broadly classified as segment models have been described for representing a variable-length sequence of observation vectors in speech recognition applications. Since there are many aspects in common between these approaches, including the general recognition and ...}, author = {Ostendorf, M. and Digalakis, V. and Kimball, O. }, citeulike-article-id = {1243548}, keywords = {generative-models, language-modeling, speech-recognition}, posted-at = {2007-04-22 13:54:45}, priority = {2}, title = {From HMMs to segment models: a unified view of stochastic modeling for speech recognition}, url = {http://citeseer.ist.psu.edu/ostendorf95from.html}, year = {1996} }

TY - GEN ID - citeulike:1243548 L3 - citeulike-article-id:1243548 N2 - In recent years, many alternative models have been proposed to address some of the shortcomings of the hidden Markov model, currently the most popular approach to speech recognition. In particular, a variety of models that could be broadly classified as segment models have been described for representing a variable-length sequence of observation vectors in speech recognition applications. Since there are many aspects in common between these approaches, including the general recognition and ... TI - From HMMs to segment models: a unified view of stochastic modeling for speech recognition KW - generative-models KW - language-modeling KW - speech-recognition AU - Ostendorf, M AU - Digalakis, V AU - Kimball, O PY - 1996/// UR - http://citeseer.ist.psu.edu/ostendorf95from.html ER -