Phonetic subspace adaptation for automatic speech recognition
Sina Hamidi Ghalehjegh, Richard Cameron Rose · 2013
An approach is proposed for adapting subspace projection vectors in the subspace Gaussian mixturemodel (SGMM) [1]. Subword models in the SGMM are composed of states, each of which are parametrized using a small number of subspace projection vectors. It is shown here that these projection vectors provide a compact and well-behaved characterization of phonetic information in speech. A regression based subspace vector adaptation approach is proposed for adapting these parameters. The performance of this approach is evaluated for unsupervised speaker adaptation on two large vocabulary speech corpora.