  Fundamental frequency is one of the most important parameters of human
speech, of importance for the classification of accent, gender, speaking
styles, speaker identification, age, among others. The proper detection of this
parameter remains as an important challenge for severely degraded signals. In
previous references for detecting fundamental frequency in noisy speech using
deep learning, the networks, such as Long Short-term Memory (LSTM) has been
initialized with random weights, and then trained following a back-propagation
through time algorithm. In this work, a proposal for a more efficient
initialization, based on a supervised training using an Auto-associative
network, is presented. This initialization is a better starting point for the
detection of fundamental frequency in noisy speech. The advantages of this
initialization are noticeable using objective measures for the accuracy of the
detection and for the training of the networks, under the presence of additive
white noise at different signal-to-noise levels.
