Browsing by Subject "Speech analysis"

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    Interframe differential coding of line spectrum frequencies
    (IEEE, 1994) Erzin, E.; Çetin, A. Enis
    Line spectrum frequencies (LSF's) uniquely represent the linear predictive coding (LPC) filter of a speech frame. In many vocoders LSF's are used to encode the LPC parameters. In this paper, an inter-frame differential coding scheme is presented for the LSF's. The LSF's of the current speech frame are predicted by using both the LSF's of the previous frame and some of the LSF's of the current frame. Then, the difference resulting from prediction is quantized.
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    Subband analysis for robust speech recognition in the presence of car noise
    (IEEE, 1995-05) Çetin, A. Enis; Yardımcı, Y.; Erzin, Engin
    In this paper, a new set of speech feature representations for robust speech recognition in the presence of car noise are proposed. These parameters are based on subband analysis of the speech signal. Line Spectral Frequency (LSF) representation of the Linear Prediction (LP) analysis in subbands and cepstral coefficients derived from subband analysis (SUBCEP) are introduced, and the performances of the new feature representations are compared to mel scale cepstral coefficients (MELCEP) in the presence of car noise. Subband analysis based parameters are observed to be more robust than the commonly employed MELCEP representations.
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    Teager energy based feature parameters for speech recognition in car noise
    (Institute of Electrical and Electronics Engineers, 1999-10) Jabloun, F.; Çetin, A. Enis; Erzin, E.
    In this letter, a new set of speech feature parameters based on multirate signal processing and the Teager energy operator is introduced. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filterbank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by log-compression and inverse discrete cosine transform (DCT) computation. The new feature parameters have robust speech recognition performance in the presence of car engine noise.