Vector quantization: A pattern-matching technique for speech coding

Gersho, A.; Cuperman, V.

doi:10.1109/mcom.1983.1091516

Cited by 159 publications

(34 citation statements)

References 15 publications

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“…Using the ideal prediction error to design the codebooks removes this dependence, hence the name open-loop approach. Iterative designs, i.e., closed-loop approaches [11], only provide a minor gain.…”

Section: A Codebook Designmentioning

confidence: 99%

“…In this paper we present a scheme that uses Predictive Vector Quantization (PVQ) [11] to exploit the correlation between successive channel realizations in order to improve the quantization, and thus to improve the sum rate of the system. Further, our scheme does not make any assumptions on the scheduling function and on the transmission strategy, which allows for a high flexibility.…”

Section: Introductionmentioning

confidence: 99%

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Feedback Compression for Correlated Broadcast Channels

Simon

Francisco

Slock

et al. 2007

2007 14th IEEE Symposium on Communications and Vehicular Technology in the Benelux

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Abstract-In this paper we apply Predictive Vector Quantization (PVQ) to quantize time-correlated broadcast channels. PVQ exploits the time-correlation of the channel to reduce the quantization error of the channels, and thus to improve the sum rate of the system. PVQ predicts the actual channel based on a number of previous channels, and then quantizes the difference between the prediction and the true channel. In this paper we show how the corresponding codebooks can be designed, and we present a prediction strategy. The performance of PVQ for a broadcast system is depicted through numerical simulations.

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Section: A Codebook Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feedback Compression for Correlated Broadcast Channels

Simon

Francisco

Slock

et al. 2007

2007 14th IEEE Symposium on Communications and Vehicular Technology in the Benelux

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“…Such an adaptive system can be very complex, but Gersho and Cuperman (1983) describe a simple method using energy and signal autocorrelation to assign speech frames to one of three classes, with each class having its own codebook. This thesis describes, in chapters 4 and 6, a similar method which uses 3 classes but only 2 codebooks; such adaptive techniques have apparently not been previously applied in speech recognition.…”

Section: Use Of Energy In Vector Quantizationmentioning

confidence: 99%

“…There are many ways that a sound segment representation of speech might be implemented, but one method that has become increasingly popular in recent years is known as vector quantization (Gersho and Cuperman, 1983).…”

mentioning

confidence: 99%

Speech recognition using a synthesized codebook

Smith¹

1988

The Journal of the Acoustical Society of America

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Speech sounds generated by a simple waveform synthesizer were used to create a vector quantization codebook for use in speech recognition. Recognition was tested over the TI-20 isolated word database using a conventional DTW matching algorithm. Input speech was filtered to limit the bandwidth to 300–3300 Hz, then was passed through the Scott Instruments Coretechs process, implemented on the SI2010 signal processing chip, to create the speech representation for matching. Synthesized sounds were processed in software by an SI2010 emulation program. SI2010 emulation and recognition were performed on a DEC VAX 11/750. The original codebook contained 109 vectors. This codebook was decimated through the course of the experiments, based on the number of times each vector was used in quantizing the training data for the previous experiment. Recognition scores are presented for progressively smaller codebook sizes, as well as for the baseline condition (no vector quantization).

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“…Em outras palavras, essa teoria ressalta a superioridade da quantização vetorial (QV) sobre a quantização escalar [4]. Matematicamente, a quantização vetorial pode ser definida como um mapeamento ¥ de um vetor de entrada [7,8]), é expressa em bit/amostra. Em se tratando de codificação de imagens (e. g. [6,9]), é expressa em bits por pixel (bpp).…”

Section: Introductionunclassified

Construção de Dicionários Voltados para a Redução da Complexidade Computacional da Etapa de Codificação da Quantização Vetorial

Madeiro¹,

Lopes²,

Neto³

et al. 2016

Anais Do 6. Congresso Brasileiro De Redes Neurais

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IntroduçãoA quantização vetorial [1, 2], que pode ser vista como uma extensão da quantização escalar em um espaço multidimensional, encontra-se fundamentada na Teoria da Distorção Versus Taxa [3], formulada por Shannon, segundo a qual um melhor desempenhoé obtido codificando-se blocos de amostras (istoé, vetores) ao invés de amostras individuais (istoé, escalares). Em outras palavras, essa teoria ressalta a superioridade da quantização vetorial (QV) sobre a quantização escalar [4]. Matematicamente, a quantização vetorial pode ser definida como um mapeamento ¥ de um vetor de entrada

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Vector quantization: A pattern-matching technique for speech coding

Cited by 159 publications

References 15 publications

Feedback Compression for Correlated Broadcast Channels

Feedback Compression for Correlated Broadcast Channels

Speech recognition using a synthesized codebook

Construção de Dicionários Voltados para a Redução da Complexidade Computacional da Etapa de Codificação da Quantização Vetorial

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