Artificial stereo data generation for speech feature mapping

Chang Woo Han, Tae Gyoon Kang, Shin Jae Kang, June Sig Sung, Nam Soo Kim

Abstract: Feature mapping technique is widely used to eliminate the mismatch between the training and test conditions of speech recognition. In the feature mapping, a target (mismatched) feature vector sequence is mapped closer to the corresponding reference (matched) feature vector stream. The training of the mapping system is usually carried out based on a set of stereo data which consists of simultaneous recordings obtained in both the reference and target conditions. In this paper, we propose a novel approach to blind parameter estimation which does not require the reference feature vectors. The proposed approach is motivated by the hidden Markov model (HMM)-based speech synthesis algorithm.

External IDs:dblp:conf/icassp/HanKKSK12