The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, propomos novas abordagens para o aprimoramento da fala com base na decisão suave. A fim de aumentar a confiabilidade estatística na estimativa da atividade de fala, introduzimos o conceito de probabilidade global de ausência de fala (GSAP). Primeiro, calculamos a probabilidade de ausência de fala convencional (SAP) e depois a modificamos de acordo com o GSAP recentemente proposto. A modificação é feita de forma que o SAP tenha o mesmo valor do GSAP no caso de ausência de fala e seja mantido em seu valor original quando a fala estiver presente. Além disso, para melhorar o desempenho dos SAP's nas caudas de voz (períodos de transição da fala para o silêncio), revisamos os SAP's usando um esquema de ressaca baseado no modelo oculto de Markov (HMM). Além disso, sugerimos um algoritmo robusto de atualização de ruído no qual a potência do ruído é estimada não apenas nos períodos de ausência de fala, mas também durante a atividade de fala com base na decisão suave. Além disso, para melhorar as rotinas de determinação SAP e atualização de ruído, apresentamos um novo conceito de relação sinal-ruído (SNR), que é chamado de SNR previsto neste artigo. Além disso, demonstramos que a transformada discreta de cosseno (DCT) aumenta a precisão da estimativa SAP. Vários testes mostram que o método proposto, denominado algoritmo de aprimoramento de fala baseado em decisão suave (SESD), produz melhor desempenho do que as abordagens convencionais.
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Joon-Hyuk CHANG, Nam Soo KIM, "Speech Enhancement: New Approaches to Soft Decision" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 9, pp. 1231-1240, September 2001, doi: .
Abstract: In this paper, we propose new approaches to speech enhancement based on soft decision. In order to enhance the statistical reliability in estimating speech activity, we introduce the concept of a global speech absence probability (GSAP). First, we compute the conventional speech absence probability (SAP) and then modify it according to the newly proposed GSAP. The modification is made in such a way that the SAP has the same value of GSAP in the case of speech absence while it is maintained to its original value when the speech is present. Moreover, for improving the performance of the SAP's at voice tails (transition periods from speech to silence), we revise the SAP's using a hang-over scheme based on the hidden Markov model (HMM). In addition, we suggest a robust noise update algorithm in which the noise power is estimated not only in the periods of speech absence but also during speech activity based on soft decision. Also, for improving the SAP determination and noise update routines, we present a new signal to noise ratio (SNR) concept which is called the predicted SNR in this paper. Moreover, we demonstrate that the discrete cosine transform (DCT) enhances the accuracy of the SAP estimation. A number of tests show that the proposed method which is called the speech enhancement based on soft decision (SESD) algorithm yields better performance than the conventional approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_9_1231/_p
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@ARTICLE{e84-d_9_1231,
author={Joon-Hyuk CHANG, Nam Soo KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Speech Enhancement: New Approaches to Soft Decision},
year={2001},
volume={E84-D},
number={9},
pages={1231-1240},
abstract={In this paper, we propose new approaches to speech enhancement based on soft decision. In order to enhance the statistical reliability in estimating speech activity, we introduce the concept of a global speech absence probability (GSAP). First, we compute the conventional speech absence probability (SAP) and then modify it according to the newly proposed GSAP. The modification is made in such a way that the SAP has the same value of GSAP in the case of speech absence while it is maintained to its original value when the speech is present. Moreover, for improving the performance of the SAP's at voice tails (transition periods from speech to silence), we revise the SAP's using a hang-over scheme based on the hidden Markov model (HMM). In addition, we suggest a robust noise update algorithm in which the noise power is estimated not only in the periods of speech absence but also during speech activity based on soft decision. Also, for improving the SAP determination and noise update routines, we present a new signal to noise ratio (SNR) concept which is called the predicted SNR in this paper. Moreover, we demonstrate that the discrete cosine transform (DCT) enhances the accuracy of the SAP estimation. A number of tests show that the proposed method which is called the speech enhancement based on soft decision (SESD) algorithm yields better performance than the conventional approaches.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Speech Enhancement: New Approaches to Soft Decision
T2 - IEICE TRANSACTIONS on Information
SP - 1231
EP - 1240
AU - Joon-Hyuk CHANG
AU - Nam Soo KIM
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2001
AB - In this paper, we propose new approaches to speech enhancement based on soft decision. In order to enhance the statistical reliability in estimating speech activity, we introduce the concept of a global speech absence probability (GSAP). First, we compute the conventional speech absence probability (SAP) and then modify it according to the newly proposed GSAP. The modification is made in such a way that the SAP has the same value of GSAP in the case of speech absence while it is maintained to its original value when the speech is present. Moreover, for improving the performance of the SAP's at voice tails (transition periods from speech to silence), we revise the SAP's using a hang-over scheme based on the hidden Markov model (HMM). In addition, we suggest a robust noise update algorithm in which the noise power is estimated not only in the periods of speech absence but also during speech activity based on soft decision. Also, for improving the SAP determination and noise update routines, we present a new signal to noise ratio (SNR) concept which is called the predicted SNR in this paper. Moreover, we demonstrate that the discrete cosine transform (DCT) enhances the accuracy of the SAP estimation. A number of tests show that the proposed method which is called the speech enhancement based on soft decision (SESD) algorithm yields better performance than the conventional approaches.
ER -