An HMM-based artificial bandwidth extension evaluated by cross-language training and test

Artificial bandwidth extension techniques can be employed in mobile terminals to improve the quality of the far-end speaker´s signal at the receiver. To accomplish this, usually statistical models are trained requiring wideband speech material from a language that is expected to be used in the conversation. In practice however, the language of a certain phone conversation is not known to the user equipment. Therefore we investigated the performance of an HMM- based multilingually trained artificial bandwidth extension on speech signals of which the language was unseen in training. The cross-language training and test turned out to cause only minor degradations compared to the use of monolingually trained acoustic models of the language used in test. Our findings indicate that artificial bandwidth extension can be efficiently trained with multilingual speech data without significant losses in speech quality.