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

• URL: http://arxiv.org/abs/2103.01173v1
• Date: Mon, 1 Mar 2021 18:13:23 GMT
• Title: Unsupervised Classification of Voiced Speech and Pitch Tracking Using Forward-Backward Kalman Filtering
• Authors: Benedikt Boenninghoff, Robert M. Nickel, Steffen Zeiler, Dorothea Kolossa
• Abstract summary: 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.
• Score: 14.950964357181524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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