Abstract: Vector quantization is a powerful tool for speech
coding applications. This paper deals with LPC Coding of speech
signals which uses a new technique called Multi Switched Split
Vector Quantization, This is a hybrid of two product code vector
quantization techniques namely the Multi stage vector quantization
technique, and Switched split vector quantization technique,. Multi
Switched Split Vector Quantization technique quantizes the linear
predictive coefficients in terms of line spectral frequencies. From
results it is proved that Multi Switched Split Vector Quantization
provides better trade off between bitrate and spectral distortion
performance, computational complexity and memory requirements
when compared to Switched Split Vector Quantization, Multi stage
vector quantization, and Split Vector Quantization techniques. By
employing the switching technique at each stage of the vector
quantizer the spectral distortion, computational complexity and
memory requirements were greatly reduced. Spectral distortion was
measured in dB, Computational complexity was measured in
floating point operations (flops), and memory requirements was
measured in (floats).
Abstract: Vector quantization is a powerful tool for speech
coding applications. This paper deals with LPC Coding of speech
signals which uses a new technique called Multi Switched Split
Vector Quantization (MSSVQ), which is a hybrid of Multi, switched,
split vector quantization techniques. The spectral distortion
performance, computational complexity, and memory requirements
of MSSVQ are compared to split vector quantization (SVQ), multi
stage vector quantization(MSVQ) and switched split vector
quantization (SSVQ) techniques. It has been proved from results that
MSSVQ has better spectral distortion performance, lower
computational complexity and lower memory requirements when
compared to all the above mentioned product code vector
quantization techniques. Computational complexity is measured in
floating point operations (flops), and memory requirements is
measured in (floats).
Abstract: This paper investigates the performance of a speech
recognizer in an interactive voice response system for various coded
speech signals, coded by using a vector quantization technique namely
Multi Switched Split Vector Quantization Technique. The process of
recognizing the coded output can be used in Voice banking application.
The recognition technique used for the recognition of the coded speech
signals is the Hidden Markov Model technique. The spectral distortion
performance, computational complexity, and memory requirements of
Multi Switched Split Vector Quantization Technique and the
performance of the speech recognizer at various bit rates have been
computed. From results it is found that the speech recognizer is
showing better performance at 24 bits/frame and it is found that the
percentage of recognition is being varied from 100% to 93.33% for
various bit rates.