Feature-space SVM adaptation for speaker adapted word prominence detection. (January 2019) - Schnall, Andrea; Heckmann, Martin
Highlights: New method for speaker adaptation in a prosodic prominence detection framework. Discriminative adaptation method which takes the properties of an SVM-based classifier into account. Additional regularization via Gaussanity and sparseness constraints. Detailed evaluation also in comparison to a DNN which analysis the contribution of the different regularization terms. Notable improvements of the proposed method compared to standard approaches. Abstract: Prosodic cues such as the word prominence play a fundamental role in human communication, e.g., to express important information. Since different speakers use a wide variety of features to express prominence, there is a large difference in performance between speaker dependently and speaker independently trained models. To cope with these variations without training a new speaker dependent model, in speech recognition speaker adaptation techniques such as feature-space Maximum Likelihood Linear Regression (fMLLR) turned out to be very useful. These methods are developed for GMM-HMM based classifiers under the assumption that the data can be well modeled via the mixture of a few Gaussian distributions. However, in many cases these assumptions are too restrictive. In particular a discriminative classifier such as an SVM often yields far superior results to a GMM. Therefore, we propose a new adaptation method, which adapts the data to the radial basis function kernel of the SVM. To avoid overfitting we apply two regularization terms. The first is based on fMLLR and the second is an L 1 regularization to enforce a sparse transformation matrix. We analyze the method in the context of speaker adaptation for word prominence detection, with varying amounts of adaptation data and different weights of the regularization terms. We show that our novel method clearly outperforms fMLLR-GMM and fMLLR-SVM based adaptation.