This paper analyses the performance of a large bunch of pitch detection algorithms on clean and noisy speech data. Two sets of noisy speech data are considered. One corresponds to simulated noisy data, and is obtained by adding several types of noise signals at various levels on the clean speech data of the Pitch-Tracking Database from Graz University of Technology (PTDB-TUG). The second one, SPEECON, was recorded in several different acoustic environments. The paper discusses the performance of pitch detection algorithms on the simulated noisy data, and on the real noisy data of the SPEECON corpus. Also, an analysis of the performance of the best pitch detection algorithm with respect to estimated signal-to-noise ratio (SNR) shows that very similar performance is observed on the real noisy data recorded in public places, and on the clean data with addition of babble noise.