Predicting the quality of text-to-speech systems from a large-scale feature set

We extract 1495 speech features from 2 subjectively evaluated text-to-speech (TTS) databases. These features are extracted from pitch, loudness, MFCCs, spectrals, formants, and intensity. The speech material is synthesized using up to 15 different TTS systems, some of them with up to 8 different voices. We develop quality predictors for TTS signals following two different approaches to handle the huge set of speech features: a three-step feature selection followed by a stepwise multiple linear regression and an approach based on support vector machines. The predictors are cross-validated via 3-fold cross validation (CV) and leave-one-test-out (LOTO) CV. Due to the high number of features we apply a strict CVmethod where the partitioning is realized prior to the feature scaling and feature selection steps. In comparison we also follow a semi-strict approach where the partitioning effectively takes place after these steps. In the 3-fold CV case we achieve correlations as high as .75 for strict CV and .89 for semi-strict CV. The more ambitious LOTO CV yields correlations around .80 for the male speakers whereas the results for the female voices show the need for improvement.