Two extensions to ensemble speaker and speaking environment modeling for robust automatic speech recognition
Yu Tsao, Chin‐Hui Lee · 2007
Recently an ensemble speaker and speaking environment modeling (ESSEM) approach to characterizing unknown testing environments was studied for robust speech recognition. Each environment is modeled by a super-vector consisting of the entire set of mean vectors from all Gaussian densities of a set of HMMs for a particular environment. The super-vector for a new testing environment is then obtained by an affine transformation on the ensemble super-vectors. In this paper, we propose a minimum classification error training procedure to obtain discriminative ensemble elements, and a super-vector clustering technique to achieve refined ensemble structures. We test these two extentions to ESSEM on Aurora2. In a per-utterance unsupervised adaptation mode we achieved an average WER of 4.99% from OdB to 20 dB conditions with these two extentions when compared with a 5.51% WER obtained with the ML-trained gender-dependent baseline. To our knowledge this represents the best result reported in the literature on the Aurora2 connected digit recognition task.