System and method of compression/decompressing a speech signal by using split vector quantization and scalar quantization
First Claim
1. A stored program device readable by a computer, embodying a program for causing the computer to compress acoustic features extracted from a sample of speech data, forming a feature vector signal, the stored program device comprising:
- a first linear prediction analyzer having codes causing said computer to perform a first linear prediction analysis on the feature vector signal and to generate a first error vector signal;
a vector quantizer having codes causing said computer to perform a vector quantization on the first error vector signal thereby generating a first index;
a memory for storing a first prestored vector signal corresponding to said first index, said first prestored vector signal being an approximation of the first error vector signal, the vector quantizer for further generating a residual vector signal which is the difference between the first error vector signal and the first prestored approximation vector signal;
at least one partitioned vector quantizer having codes causing said computer to perform a partitioned vector quantization on a first portion of the residual vector signal thereby generating at least one second index which corresponds to a second prestored vector signal which is an approximation of the first portion of the residual vector signal;
a scalar quantizer having codes causing said computer to perform a scalar quantization on a second portion of the residual vector signal thereby generating a third index corresponding to a prestored scalar signal which is an approximation of the second portion of the residual vector signal;
a combiner module for causing said computer to combine the first, second and third indices to form an encoded vector signal which is a compressed representation of the feature vector signal;
means for causing said computer to store or transmit said compressed representation of the feature vector signal; and
a primary vector codebook, responsive to the vector quantizer, containing indexed values representing prestored approximation vector signals wherein each indexed value and, thus, each prestored approximation vector signal corresponds to a particular index, wherein the indexed values in the primary vector codebook form a tree-structured arrangement wherein the indexed values are separated into groups with a group mean vector signal being generated and stored from the average of the prestored vector signals within the group such that the vector quantizer first performs an inter-group search to locate the group of indexed values corresponding to the prestored group mean vector signal which most closely approximates the first error vector signal and then performs an intra-group search to locate the indexed value corresponding to the particular prestored vector signal which most closely approximates the first error vector signal, such prestored vector signal serving as the first prestored approximation vector signal.
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Abstract
Apparatus for processing acoustic features extracted from a sample of speech data forming a feature vector signal every frame period includes a first linear prediction analyzer, a vector quantizer, at least one partitioned vector quantizer and a scalar quantizer. The first linear prediction analyzer performs a linear prediction analysis on the feature vector signal to generate a first error vector signal. Next, the vector quantizer performs a vector quantization on the first error signal thereby generating a first index corresponding to a first prestored vector signal which is an approximation of the first error vector signal. The vector quantizer also generates a residual vector signal which is the difference between the first error vector signal and the first prestored approximation vector signal. Next, the at least one partitioned vector quantizer performs a partitioned vector quantization on a first portion of the residual vector signal thereby generating at least one second index corresponding to a second prestored vector signal which is an approximation of the first portion of the residual vector signal. Next, the scalar quantizer performs a scalar quantization on a second portion of the residual vector signal thereby generating a third index corresponding to a prestored scalar signal which is an approximation of the second portion of the residual vector signal. The first, second and third indices are combined to form an encoded vector signal which is a compressed representation of the feature vector signal. The encoded vector signal may be transmitted and/or stored as desired. The feature vector signal may be reconstructed from the encoded vector signal by adding the corresponding prestored signals to the encoded vector signal to form a decompressed representation of the feature vector signal.
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Citations
8 Claims
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1. A stored program device readable by a computer, embodying a program for causing the computer to compress acoustic features extracted from a sample of speech data, forming a feature vector signal, the stored program device comprising:
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a first linear prediction analyzer having codes causing said computer to perform a first linear prediction analysis on the feature vector signal and to generate a first error vector signal; a vector quantizer having codes causing said computer to perform a vector quantization on the first error vector signal thereby generating a first index;
a memory for storing a first prestored vector signal corresponding to said first index, said first prestored vector signal being an approximation of the first error vector signal, the vector quantizer for further generating a residual vector signal which is the difference between the first error vector signal and the first prestored approximation vector signal;at least one partitioned vector quantizer having codes causing said computer to perform a partitioned vector quantization on a first portion of the residual vector signal thereby generating at least one second index which corresponds to a second prestored vector signal which is an approximation of the first portion of the residual vector signal; a scalar quantizer having codes causing said computer to perform a scalar quantization on a second portion of the residual vector signal thereby generating a third index corresponding to a prestored scalar signal which is an approximation of the second portion of the residual vector signal; a combiner module for causing said computer to combine the first, second and third indices to form an encoded vector signal which is a compressed representation of the feature vector signal; means for causing said computer to store or transmit said compressed representation of the feature vector signal; and a primary vector codebook, responsive to the vector quantizer, containing indexed values representing prestored approximation vector signals wherein each indexed value and, thus, each prestored approximation vector signal corresponds to a particular index, wherein the indexed values in the primary vector codebook form a tree-structured arrangement wherein the indexed values are separated into groups with a group mean vector signal being generated and stored from the average of the prestored vector signals within the group such that the vector quantizer first performs an inter-group search to locate the group of indexed values corresponding to the prestored group mean vector signal which most closely approximates the first error vector signal and then performs an intra-group search to locate the indexed value corresponding to the particular prestored vector signal which most closely approximates the first error vector signal, such prestored vector signal serving as the first prestored approximation vector signal. - View Dependent Claims (2)
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3. A stored program device readable by a computer, embodying a program for causing the computer to compress acoustic features extracted from a sample of speech data, forming a feature vector signal, the stored program device comprising:
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a first linear prediction analyzer having codes causing said computer to perform a first linear prediction analysis on the feature vector signal and to generate a first error vector signal; a vector quantizer having codes causing said computer to perform a vector quantization on the first error vector signal thereby generating a first index;
a memory for storing a first prestored vector signal corresponding to said first index, said first prestored vector signal being an approximation of the first error vector signal, the vector quantizer for further generating a residual vector signal which is the difference between the first error vector signal and the first prestored approximation vector signal;at least one partitioned vector quantizer having codes causing said computer to perform a partitioned vector quantization on a first portion of the residual vector signal thereby generating at least one second index which corresponds to a second prestored vector signal which is an approximation of the first portion of the residual vector signal; a scalar quantizer having codes causing said computer to perform a scalar quantization on a second portion of the residual vector signal thereby generating a third index corresponding to a prestored scalar signal which is an approximation of the second portion of the residual vector signal; a combiner module for causing said computer to combine the first, second and third indices to form an encoded vector signal which is a compressed representation of the feature vector signal; means for causing said computer to store or transmit said compressed representation of the feature vector signal; and at least one secondary vector codebook, responsive to the at least one partitioned vector quantizer, containing indexed values representing prestored approximation vector signals wherein each indexed value and, thus, each prestored approximation vector signal corresponds to a particular index, wherein the indexed values in the at least one secondary vector codebook form a tree-structured arrangement wherein the indexed values are separated into groups with a group means vector signal being generated and stored from the average of the prestored vector signals within the group such that the at least one partitioned vector quantizer first performs an inter-group search to locate the group of indexed values corresponding to the prestored group mean vector signal which most closely approximates the first portion of the residual vector signal and then performs an intra-group search to locate the indexed value corresponding to the particular prestored vector signal which most closely approximates the first portion of the residual vector signal, such prestored vector signal serving as the second prestored approximation vector signal. - View Dependent Claims (4, 5, 6)
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7. A stored program device accessible by a computer, having instructions executable by said computer to perform method steps for processing acoustic features extracted from a sample of speech data forming a feature vector signal, the method steps comprising:
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a) performing a first linear prediction analysis on the feature vector signal to generate a first error vector signal in response thereto; b) performing vector quantization on the first error vector signal thereby generating a first index which corresponds to a first prestored vector signal which is an approximation of the first error vector signal, the vector quantization sub-process also generating a residual vector signal which is the difference between the first error vector signal and the first prestored approximation vector signal; c) performing partitioned vector quantization on a first portion of the residual vector signal thereby generating at least one second index which corresponds to a second prestored vector signal which is an approximation of the first portion of the residual vector signal; d) performing scalar quantization on a second portion of the residual vector signal thereby generating a third index corresponding to a prestored scalar signal which is an approximation of the second portion of the residual vector signal; e) combining the first, second and third indices to form an encoded vector signal which is a compressed representation of the feature vector signal; f) responding to the vector quantizer with a primary vector codebook containing indexed values representing prestored approximation vector signals wherein each indexed value and, thus, each prestored approximation vector signal corresponds to a particular index; g) forming a tree-structured arrangement with the indexed values in the primary vector codebook wherein the indexed values are separated into groups with a group mean vector signal being generated and stored from the average of the prestored vector signals within the group such that the vector quantizer first performs an inter-group search to locate the group of indexed values corresponding to the prestored group mean vector signal which most closely approximates the first error vector signal and then performs an intra-group search to locate the indexed value corresponding to the particular prestored vector signal which most closely approximates the first error vector signal, such prestored vector signal serving as the first prestored approximation vector signal; and h) storing in memory or transmitting over a data transmission medium said encoded vector signal. - View Dependent Claims (8)
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Specification