Abstract
Pitch tracking in noisy speech is a challenging task as temporal and spectral patterns of the speech signal are both corrupted. This paper proposes long short-term memory (LSTM) based methods for pitch probability estimation. Two architectures are investigated. The first one is conventional LSTM that utilizes recurrent connections to model pitch dynamics. The second one is two-level time-frequency LSTM, with the first level scanning frequency bands and the second level connecting the first level through time. The Viterbi algorithm then takes the probabilistic output from LSTM to generate continuous pitch contours. Experiments show that both proposed models outperform a deep neural network (DNN) based model in most conditions. Time-frequency LSTM achieves the best performance at negative SNRs.
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