Implementing vocal tract length normalization in the MLLR framework

  title={Implementing vocal tract length normalization in the MLLR framework},
  author={Guo-Hong Ding and Yi-Fei Zhu and Chengrong Li and Bo Xu},
Vocal Tract Length Normalization (VTLN) and Maximum Likelihood Linear Regression (MLLR) are two approaches to reduce the degradation in speech recognition performance caused by variation of speakers. This paper derives a novel efficient adaptation algorithm from the two techniques. Based on prior knowledge of usual VTLN, an approximate constrained-form linear transformation is obtained. The transformation is learned using EM algorithm and then applied in the MLLR setting. Experiments of three… CONTINUE READING