Unsupervised Classification of Voiced Speech and Pitch Tracking Using Forward-Backward Kalman Filtering

Konferenz: Speech Communication - 12. ITG-Fachtagung Sprachkommunikation
05.10.2016 - 07.10.2016 in Paderborn, Deutschland

Tagungsband: ITG-Fb. 267: Speech Communication

Seiten: 5Sprache: EnglischTyp: PDF

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Boenninghoff, Benedikt T.; Zeiler, Steffen; Kolossa, Dorothea (Institute of Communication Acoustics, Ruhr-Universität Bochum, 44780 Bochum, Ge)
Nickel, Robert M. (Department of Electrical and Computer Engineering, Bucknell University, Lewisburg, PA 17837, USA)

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.