An attempt to create speech synthesis model that retains Lombard effect characteristics

The speech with the Lombard effect has been extensively studied in the context of speech recognition or speech enhancement. However, few studies have investigated the Lombard effect in the context of speech synthesis. The aim of this paper is to create a mathematical model that allows for retaining the Lombard effect. These models could be used as a basis of a formant speech synthesizer. The proposed models are based on dividing the speech signal into harmonics and modeling them as the output of a SISO system whose transfer function poles are multiple, and inputs vary in time. An analysis of the Lombard effect of the synthesized signal is performed on the noise residual. The synthesized signal residual is described by vectors of acoustic parameters related to the Lombard effect. For testing the performance of the created models in various noise conditions two classifiers are employed, namely kNN and Naive Bayes. For comparison of results, we created models of sinusoids based on frequency tracks. The results show that a model based on the residual sinewave sum demonstrates the possibility of retaining the Lombard effect. Finally, future work directions are outlined in conclusions