Abstract: A time-varying pitch of a signal may be estimated by processing a sequence of frames of the speech signal. An estimated fractional chirp rate may be computed for each frame of the sequence of frames, and the estimated fractional chirp rates may be used to compute a pitch template for the sequence, where the pitch template indicates the time-varying pitch of the signal subject to a scale factor. A first pitch estimate for each frame of the sequence of frames may be computed by computing a scale factor and multiplying the pitch template by the scale factor. A second pitch estimate may be computed from the first pitch estimate by identifying peaks in the frequency representations using the first pitch estimates and fitting a parametric function to the peaks.

Abstract: A time-varying pitch of a signal may be estimated by processing a sequence of frames of the speech signal. An estimated fractional chirp rate may be computed for each frame of the sequence of frames, and the estimated fractional chirp rates may be used to compute a pitch template for the sequence, where the pitch template indicates the time-varying pitch of the signal subject to a scale factor. A first pitch estimate for each frame of the sequence of frames may be computed by computing a scale factor and multiplying the pitch template by the scale factor. A second pitch estimate may be computed from the first pitch estimate by identifying peaks in the frequency representations using the first pitch estimates and fitting a parametric function to the peaks.

Abstract: A time-varying pitch of a signal may be estimated by processing a sequence of frames of the speech signal. An estimated fractional chirp rate may be computed for each frame of the sequence of frames, and the estimated fractional chirp rates may be used to compute a pitch template for the sequence, where the pitch template indicates the time-varying pitch of the signal subject to a scale factor. A first pitch estimate for each frame of the sequence of frames may be computed by computing a scale factor and multiplying the pitch template by the scale factor. A second pitch estimate may be computed from the first pitch estimate by identifying peaks in the frequency representations using the first pitch estimates and fitting a parametric function to the peaks.

Abstract: Input data may be classified using one or both of mutual information between segments and expected class scores. Input data to be classified may be segmented into an input sequence of segments. The input sequence of segments may be compared with a reference sequences of segments for a first class to generate a first class score indicating a similarity between the input data and the first class. The first class score may be computed by computing a probability mass function between input segments and reference segments and then computing a mutual information value from the probability mass function. The input data may then be classified using the first class score and/or class score for other classes. In some implementations, expected class scores may be used in making the classification decision.

Abstract: A time-varying pitch of a signal may be estimated by processing a sequence of frames of the speech signal. An estimated fractional chirp rate may be computed for each frame of the sequence of frames, and the estimated fractional chirp rates may be used to compute a pitch template for the sequence, where the pitch template indicates the time-varying pitch of the signal subject to a scale factor. A first pitch estimate for each frame of the sequence of frames may be computed by computing a scale factor and multiplying the pitch template by the scale factor. A second pitch estimate may be computed from the first pitch estimate by identifying peaks in the frequency representations using the first pitch estimates and fitting a parametric function to the peaks.

Abstract: The technology described in this document can be embodied in a computer-implemented method that includes obtaining a speech signal, and estimating a first set and a second set of segment boundaries using the speech signal. The first and second set of segment boundaries are determined using a first and second segmentation process, respectively. The second segmentation process is different from the first segmentation process. The method also includes obtaining a model corresponding to a distribution of segment boundaries, computing a first score indicative of a degree of similarity between the model and the first set of segment boundaries, and computing a second score indicating a degree of similarity between the model and the second set of segment boundaries. The method further includes selecting a set of segment boundaries using the first score and the second score, and processing the speech signal using the selected set of segment boundaries.

Abstract: The technology described in this document can be embodied in a computer-implemented method that includes obtaining a plurality of portions of a speech signal, and obtaining a plurality of frequency representations by computing a frequency representation of each portion of the speech signal. The method also includes generating, by one or more processing devices, a time-varying data set using the plurality of frequency representations by computing an entropy of each frequency representation of the plurality of frequency representations, and determining, by the one or more processing devices, boundaries of a speech segment using the time-varying data set. The method further includes classifying the speech segment into a first class of a plurality of classes, and processing the speech signal using the first class of the speech segment.