  Pitch or fundamental frequency (f0) extraction is a fundamental problem
studied extensively for its potential applications in speech and clinical
applications. In literature, explicit mode specific (modal speech or singing
voice or emotional/ expressive speech or noisy speech) signal processing and
deep learning f0 extraction methods that exploit the quasi periodic nature of
the signal in time, harmonic property in spectral or combined form to extract
the pitch is developed. Hence, there is no single unified method which can
reliably extract the pitch from various modes of the acoustic signal. In this
work, we propose a hybrid f0 extraction method which seamlessly extracts the
pitch across modes of speech production with very high accuracy required for
many applications. The proposed hybrid model exploits the advantages of deep
learning and signal processing methods to minimize the pitch detection error
and adopts to various modes of acoustic signal. Specifically, we propose an
ordinal regression convolutional neural networks to map the periodicity rich
input representation to obtain the nominal pitch classes which drastically
reduces the number of classes required for pitch detection unlike other deep
learning approaches. Further, the accurate f0 is estimated from the nominal
pitch class labels by filtering and autocorrelation. We show that the proposed
method generalizes to the unseen modes of voice production and various noises
for large scale datasets. Also, the proposed hybrid model significantly reduces
the learning parameters required to train the deep model compared to other
methods. Furthermore,the evaluation measures showed that the proposed method is
significantly better than the state-of-the-art signal processing and deep
learning approaches.
