I-vector based utterance verification for large-vocabulary speech recognition system

This paper proposes a new Utterance Verification (UV) algorithm based on i-vector. Phone segments are extracted and concatenated from the training data, which are used to train the Universal Background Model (UBM) and the Total Variability (TV) matrix, and then, i-vector is extracted from the enrollment and evaluation data using UBM and TV matrix. We compare two Confidence Measures (CMs), cosine distance scoring and Support Vector Machine (SVM). To compensate the channel effect, we use two channel compensation methods, Linear Discriminant Analysis (LDA) and Within-Class Covariance Normalization (WCCN). The decision is made by the word-level CM by combining the phone-level CMs. Experiments are conducted in the Korean isolated word recognition domain. Experimental results show that SVM is superior to cosine distance scoring. Best performance is achieved when SVM is used without any channel compensation method.