6 research outputs found
Interframe differential vector coding of line spectrum frequencies
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 interframe 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 vector resulting from prediction is vector quantized
Interframe differential coding of line spectrum frequencies
Cataloged from PDF version of article.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
Line spectral frequency representation of subbands for speech recognition
In this paper, a new set of speech feature parameters is constructed from subband analysis based Line Spectral Frequencies (LSFs). The speech signal is divided into several subbands and the resulting subsignals are represented by LSFs. The performance of the new speech feature parameters, SUBLSFs, is compared with the widely used Mel Scale Cepstral Coefficients (MELCEPs). SUBLSFs are observed to be more robust than the MELCEPs in the presence of car noise. © 1995
Subband analysis for robust speech recognition in the presence of car noise
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
New methods for robust speech recognition
Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 1995.Thesis (Ph.D.) -- Bilkent University, 1995.Includes bibliographical references leaves 86-92.New methods of feature extraction, end-point detection and speech enhcincement
are developed for a robust speech recognition system.
The methods of feature extraction and end-point detection are based on
wavelet analysis or subband analysis of the speech signal. Two new sets of speech
feature parameters, SUBLSF’s and SUBCEP’s, are introduced. Both parameter
sets are based on subband analysis. The SUBLSF feature parameters are obtained
via linear predictive analysis on subbands. These speech feature parameters
can produce better results than the full-band parameters when the noise is
colored. The SUBCEP parameters are based on wavelet analysis or equivalently
the multirate subband analysis of the speech signal. The SUBCEP parameters
also provide robust recognition performance by appropriately deemphasizing the
frequency bands corrupted by noise. It is experimentally observed that the
subband analysis based feature parameters are more robust than the commonly
used full-band analysis based parameters in the presence of car noise.
The a-stable random processes can be used to model the impulsive nature of the public network telecommunication noise. Adaptive filtering are developed
for Q-stable random processes. Adaptive noise cancelation techniques are used to
reduce the mismacth between training and testing conditions of the recognition
system over telephone lines.
Another important problem in isolated speech recognition is to determine
the boundaries of the speech utterances or words. Precise boundary detection
of utterances improves the performance of speech recognition systems. A new
distance measure based on the subband energy levels is introduced for endpoint
detection.Erzin, EnginPh.D
Speech spectrum non-stationarity detection based on line spectrum frequencies and related applications
Ankara : Department of Electrical and Electronics Engineering and The Institute of Engineering and Sciences of Bilkent University, 1998.Thesis (Master's) -- Bilkent University, 1998.Includes bibliographical references leaves 124-132In this thesis, two new speech variation measures for speech spectrum nonstationarity
detection are proposed. These measures are based on the Line
Spectrum Frequencies (LSF) and the spectral values at the LSF locations.
They are formulated to be subjectively meaningful, mathematically tractable,
and also have low computational complexity property. In order to demonstrate
the usefulness of the non-stationarity detector, two applications are presented:
The first application is an implicit speech segmentation system which detects
non-stationary regions in speech signal and obtains the boundaries of the speech
segments. The other application is a Variable Bit-Rate Mixed Excitation Linear
Predictive (VBR-MELP) vocoder utilizing a novel voice activity detector
to detect silent regions in the speech. This voice activity detector is designed
to be robust to non-stationary background noise and provides efficient coding
of silent sections and unvoiced utterances to decrease the bit-rate. Simulation
results are also presented.Ertan, Ali ErdemM.S