HMM2- A NOVEL APPROACH TO HMM EMISSION PROBABILITY ESTIMATION
作者单位：IDIAP-Dalle Molle Institute of Perceptual Artificial Intelligence Martigny Switzerland EPFL-Swiss Federal Institute of Technology Lausanne Switzerland IDIAP-Dalle Molle Institute of Perceptual Artificial Intelligence Martigny Switzerland IDIAP-Dalle Molle Institute of Perceptual Artificial Intelligence Martigny Switzerland EPFL-Swiss Federal Institute of Technology Lausanne Switzerland
会议名称：《6～(th) International Conference on Spoken Language Processing》
会议日期：2000年
摘 要：正 In this paper, we discuss and investigate a new method to estimate local emission probabilities in the framework of hidden Markov models （HMM）. Each feature vector is considered to be a sequence and is supposed to be modeled by yet another HMM. Therefore, we call this approach ’HMM2’. There is a variety of possible topologies of such HMM2 systems, e.g. incorporating trellis or ergodic HMM structures. Preliminary HMM2 speech recognition experiments on cepstral and spectral features yielded worse results than state-of the-art systems. However, we believe that HMM2 systems have a lot of potential advantages and are therefore worth investigating further.