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    Thursday, November 17, 2005

    Speech Analysis using LPC

    Writting a speech analysis tool is not difficult if you choose a good software you are familiar to. Some useful software could be found at the sidebar of this blog. I have written some tools for speech signal analysis using MATLAB for the sake of speech signal studies. It could be found at the matlabcentral:


    This GUI is designed to extract and visualize the spectrum of FFT and LPC of a specific frame, or a window. Following figure shows the LPC spectrum of a frame with 256 samples. The indicator on the upper subplot shows the location of the specific frame.

    Saturday, November 05, 2005

    Frames/Blocks Processing

    The LPC spectral analysis which has been discussed in the previous post section can be used to analyse the spectrum of each frame. Figure below illustrates the LPC spectra of a speech signal from frame 48 to frame 63.

    This segment of speech signal by a male speaker is the voiced region, which corresponds to the diphthong “ay” in digit “five”. A few characteristics can be found from the graph. Firstly, the locations of three formants for all frames are almost the same. Secondly, the amplitudes of the first formant of all frames are respectively high (Compare to unvoiced frames shown below).

    Frames Representation of Speech Signal

    Frame-based data is a common format in digital computers. Data acquisition hardware often operates by accumulating a large number of signal samples at a high rate, and propagating these samples to the digital computer as a block of data.

    There are some reasons of doing frame processing. Firstly, some time-properties of the signal are easier to be seen in frames. For example, the energy level of a speech signal for a period of time is analyzed in frames for a few milliseconds. Secondly, most of the analyses in frequency domain, for example, short-time Fourier transform, needs the data to be in blocks, or windows. Another advantage of frame analysis is the application in real-time system. The frame processing maximizes the efficiency of the system by distributing the fixed process overhead across many samples; the fast data acquisition is suspended by slow interrupt processes after each frame is acquired, rather than after each individual sample. Typical values of parameters are applied for the frame processing in which frame size is 256 samples with the overlapping of 64 samples for the 8 kHz signal. The figure below illustrates a segment of speech signal is split into frames with 256 samples per frame and 192 overlapping.

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