Factored MLLR Adaptation

Abstract

One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In this letter, we extend MLLR to factored MLLR (FMLLR) in which the MLLR parameters depend on a continuous-valued control vector. Since it is practically impossible to estimate the MLLR parameters for each control vector separately, we propose a compact parametric form of the MLLR parameters. In the proposed approach, each MLLR parameter is represented as an inner product between a regression vector and transformed control vector. We present an algorithm to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. The proposed approach is applied to adapt the HMM parameters obtained from a database of reading-style speech to singing-style voices while treating the pitches and durations extracted from the musical notes as the control vectors. This enables to efficiently construct a singing voice synthesizer with only a small amount of singing data.