A frequent phenomen in spoken dialogs of the information seeking type are short elliptic utterances whose mood (declarative or interrogative) can only be distinguished by intonation. The main acoustic evidence is conveyed by the fundamental frequency or F0 contour. Many algorithms for F0 determination have been reported in the literature. A common problem are irregularities of speech known as laryngealizations. This article describes an approach based on neuronal network techniques for the improved determination of fundamental frequency. First, an improved version of our neuronal network algorithm for reconstruction of the voice source signal (glottis signal) is presented. Second, the reconstructed voice source signal is used as input to another neuronal network destinguishing the three classes 'voiceless', 'voiced-non-laryngealized', and 'voiced-laryngealized'. Third, the results are used to improve an existing F0 algorithm. Results of this approach are presented and discussed in the context of the application in a spoken dialog system.