  The detection of voiced speech, the estimation of the fundamental frequency,
and the tracking of pitch values over time are crucial subtasks for a variety
of speech processing techniques. Many different algorithms have been developed
for each of the three subtasks. We present a new algorithm that integrates the
three subtasks into a single procedure. The algorithm can be applied to
pre-recorded speech utterances in the presence of considerable amounts of
background noise. We combine a collection of standard metrics, such as the
zero-crossing rate, for example, to formulate an unsupervised voicing
classifier. The estimation of pitch values is accomplished with a hybrid
autocorrelation-based technique. We propose a forward-backward Kalman filter to
smooth the estimated pitch contour. In experiments, we are able to show that
the proposed method compares favorably with current, state-of-the-art pitch
detection algorithms.
