  The fundamental frequency (F0) contour of speech is a key aspect to represent
speech prosody that finds use in speech and spoken language analysis such as
voice conversion and speech synthesis as well as speaker and language
identification. This work proposes new methods to estimate the F0 contour of
speech using deep neural networks (DNNs) and recurrent neural networks (RNNs).
They are trained using supervised learning with the ground truth of F0
contours. The latest prior research addresses this problem first as a
frame-by-frame-classification problem followed by sequence tracking using deep
neural network hidden Markov model (DNN-HMM) hybrid architecture. This study,
however, tackles the problem as a regression problem instead, in order to
obtain F0 contours with higher frequency resolution from clean and noisy
speech. Experiments using PTDB-TUG corpus contaminated with additive noise
(NOISEX-92) show the proposed method improves gross pitch error (GPE) by more
than 25 % at signal-to-noise ratios (SNRs) between -10 dB and +10 dB as
compared with one of the most noise-robust F0 trackers, PEFAC. Furthermore, the
performance on fine pitch error (FPE) is improved by approximately 20 % against
a state-of-the-art DNN-HMM-based approach.
