Elderly acoustic model for large vocabulary continuous speech recognition

Abstract

In this paper， we evaluate elderly speaker acoustic models in LVCSR， which are trained by the 301 elderly speakers' database from the age of 60 to 90.Each speaker utters 200 sentences.The elderly speaker PTM (Phonetic Tied Mixture) acoustic model attains 88.9% word recognition rate， which is better than 86.0%word recognition rate by the usual adult (an average age of 28.6) PTM acoustic model.To achieve higher recognition rates， we use two types of speaker adaptation methods， which are a su pervised MLLR and an unsupervised adaptation method based on the sufficient HMM statistics.In our exper imental results， the elderly acoustic model is better as the adaptation baseline HMM model than the usual adult model for elderly speakers.