Evaluation of Pitch Detection Algorithms in Adverse Conditions

Robust fundamental frequency estimation in adverse conditions is important in various speech processing applications. In this paper a new pitch detection algorithm (PDA) based on the autocorrelation of the Hilbert envelope of the LP residual [1] is compared to another well established algorithm from Goncharoff [2]. A set of evaluation criteria is collected on which the two PDA algorithms are compared. In order to evaluate the algorithms in adverse conditions a suited reference database was constructed. This reference database consists of parts of the SPEECON speech database [3] where recordings of 60 speakers were selected and manually pitch marked. The recordings cover several adverse conditions as noise in the car cabin and reverberations of office rooms. The evaluation highlights the good performance of the new algorithm in comparison but shows, that low SNR conditions and strong reverberation are still a demanding challenge for future pitch detection algorithms. 1