• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2516-2518
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• 2001

The quantization of the LPC parameters is a very important aspect of the speech compression algorithm. This paper analyzes the quantization effect of the LPC coefficients and presents the implementation of a fixed-point CELP coder based on the LPC analysis.

• The Journal of the Acoustical Society of Korea
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• v.31 no.2
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• pp.93-99
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• 2012

For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.205-209
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• 1994

This paper presents a variable rate speech coding method with phonetic segmentation, called for PSVXC. Multiple access techniques that require efficient encoding of speech to achieve capacity improvements are currently emerging in the cellular telephone system. The variable rate speech coder have the reduced average data rate required to transmit conversational speech. Each frame of active speech is classified into one of four phonetic classes. A distinct coding configuration and bit-rate is applied to each category. And also a split vector quantization is used to accurately quantize the LPC information using LSP parameters.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.4
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• pp.50-56
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• 1997

In this paper, efficient quantization methods of line spectrum pairs (LSP) which has good performances and low complexity and memory are proosed for vocoder of mobile communication system. The adaptive quantization method utilizing the ordering property of LSP parameters is used in a scalar quantizer and a vector-scalar hybrid quantizer. The proposed scalar quantization algorithm needs 31 bits/frame to maintain the transparent quality of speech. The improved vector-scalar quantizer achieves an average spectral distortion of 1dB using 26 bits/frame. The proposed methods are evaluated in the channel errors and changed the predictor structure to maintain the robustness to channel errors.

• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.345-352
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• 1995

The LPC cepstrum coefficients, which are an acoustic features of speech signal, have been widely used as the feature parameter for various speech recognition systems and showed good performance. The vocal tract area function is a kind of articulatory feature, which is related with the physiological mechanism of speech production. This paper proposes the vocal tract area function as an alternative feature parameter for speech recognition. The linear predictive analysis using Burg algorithm and the vector quantization are performed. Then, recognition experiments for 5 Korean vowels and 10 digits are executed using the conventional LPC cepstrum coefficients and the vocal tract area function. The recognitions using the area function showed the slightly better results than those using the conventional LPC cepstrum coefficients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.4
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• pp.649-655
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• 2006

This paper proposes a HMM-based recognition method using DMSVQ(Dynamic Multi-Section Vector Quantization) codebook by DMS model and double spectral feature, as a method on the speech recognition of speaker-independent. LPC cepstrum parameter is used as a instantaneous spectral feature and LPC cepstrum's regression coefficient is used as a dynamic spectral feature These two spectral features are quantized as each VQ codebook. HMM using DMS model is modeled by receiving instantaneous spectral feature and dynamic spectral feature by input. Other experiments to compare with the results of recognition experiments using proposed method are implemented by the various conventional recognition methods under the equivalent environment of data and conditions. Through the experiment results, it is proved that the proposed method in this paper is superior to the conventional recognition methods.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1015-1018
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• 1999

This paper describes a vector quantization for speech signal coding using neural networks. We processed speech signal using LPC method that extracts speech signal feature, and speech signal feature is quantized using competitive neural network kohonen self-organization feature map.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.761-771
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• 1994

This paper focuses on how spectrum information can be represented efficiently in a very low bit rate CELP speech coder. To achieve the goal, an LPC cepstral coefficients VQ scheme representing the spectrum information in a CELP coder is proposed. To represent the spectrum information using LPC cepstrums, three different cepstral distance measures having different spectral meanings in the frequency domain are considered, and their performances are compared and analyzed. The experimental results show that spectrum information in low bit rate CELP coders can be represented very efficiently using the proposed LPC cepstral vector quantization scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.234-240
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• 2007

In this paper, low complexity block constrained trellis coded quantization (BC-TCQ) structures are introduced, and a predictive BC TCQ encoding method is developed for quantization of line spectrum frequencies (LSF) parameters for narrowband speech coding applications. Trellis-coded quantization(TCQ) is a form of VQ that builds the VQ codebook from interleaved constituent scalar quantization codebooks. The performance is compared to the other VQ, demonstrating reduction in spectral distortion and significant reduction in encoding complexity. The predictive BC-TCQ is about 0.47107 dB superior to the IS-641 split-VQ, 26bits/frame, in spectral distortion sense. The BC-TCQ is 64.54%, 76.93%, 2.35% of the IS-641 split-VQ, respectively, in the complexity of the additions, multiplies, comparisons.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.787-790
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• 2000

This paper introduces a new type of representation of the LSPs as a promising alternative used for transmitting the LPC parameters. Major contribution in this paper is that the vocal track information embedded on the spectral envelope can be represented in terms of the reduced number of LSF compared tn the conventional. Hence, it provides a possibility that LPC parameters could be quantized at a reduced bit rate without causing any major spectral distortion. The simulation result illustrates the capability of the proposed LSPs representation as an efficient quantization method via a proper rejection of the redundant pairs of pole and zero along the unit circle.