A new speech enhancement adaptive algorithm based on fullband–subband MSE switching

Sayoud, Akila; Djendi, Mohamed; Guessoum, Abderrezak

doi:10.1007/s10772-019-09651-4

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…In continuous speech, the speed of speech of different speakers in different contexts is very different. Excessive deviations from normal speech speed tend to cause recognition errors [1]. Excessive speech speed will increase deletion errors, while too slow speech speed will increase insertion errors, thus degrading recognition performance.…”

Section: Introductionmentioning

confidence: 99%

“…SPE is described emphatically from the angle of the vocal voice production process. GP is through the acoustic spectrum analysis and draw out the phonemes in 11 kinds of complementary components, through the combination of these components can be described and distinguish all the phonemes [1,15]. To a certain extent, the redundant information and noise suppression characteristics [16] to identify units related to distinguish sexual information is retained in the probabilistic event [17,18].…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] English Audio Language Retrieval Based on Adaptive Speech‐Adjusting Algorithm

Feng

Zhou

2021

Complexity

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For the purpose of language retrieval for English listening, this paper designs and implements a cross-language information retrieval system for English listening. Different implementation methods of cross-language information retrieval, query, and translation are analyzed. The system adopts cross-language information retrieval technology based on bilingual dictionaries. According to the cross-language retrieval system of the existing bilingual dictionaries and monolingual dictionaries, based on the design and implementation of the fuzzy search dictionary lookup mechanism, the existing dictionary lookup mechanism is constructed and analyzed. Aiming at the problem of translation ambiguity in information retrieval systems based on bilingual dictionaries, a disambiguities elimination algorithm based on cooccurrence technology is proposed. In continuous speech, the speed of different speakers in different contexts is very different. Deviation from normal speech speed often leads to recognition errors, which makes recognition performance decline. Considering that the influence of speech speed on the length of speech units increases or decreases synchronously, and there is a strong correlation between the lengths of adjacent speech units, an adaptive speech speed algorithm is proposed based on the framework of implicit Markov model based on the information of the length of speech units. Experiments on number string and large vocabulary continuous speech recognition show that the algorithm is effective.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] English Audio Language Retrieval Based on Adaptive Speech‐Adjusting Algorithm

Feng

Zhou

2021

Complexity

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“…The objective in solving the BSS problem is to recover a set of signals called source signals from a set of received signals called mixtures without prior knowledge either of the nature of the source signals or of the mechanism by which the mixing system (propagation medium) mixes the signals. BSS is a very important discipline and it is involved in many research areas such as Wireless Communication [5], Acoustics [6][7][8], Seismic signal analysis [9], Biomedical field [10] ...etc. Blindly and via the BSS, the principle of the solution is based on hypotheses concerning the mixing system as well as hypotheses on the nature of the source signals.…”

Section: Introductionmentioning

confidence: 99%

Source recovery by analytical maximization of phase-shifted kurtosis for the mixtures of noisy and noiseless signals

SMATTI

Arar

2023

Preprint

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This manuscript presents a work that provides a study as well as a simple analytical solution for solving the blind source separation problem (BSS) for noiseless and noisy linear mixing of statistically independent stationary and nonstationary signals. The study is based on the exploitation of the probabilistic characteristics of the mixed signals by using the statistics of the second order and the fourth order for the completion of the separation. The proposed solution consists mainly of two steps based on the concept of the geometric solution. For the case of the mixture of two sources (2×2), the first step aims to transform the dependent signals into orthogonal signals (whitening) via the principal component analysis (PCA) principle. After the application of the PCA and in order to complete the statistical independence of the two uncorrelated signals, the second step aims to determine an adequate rotating angle that leads directly to the separation, and this angle is determined in this work analytically by the simple calculation of the phase shift of a sinusoidal objective function based on the sum of the kurtosis of the whitened signals. In the case of several sources (n×n), the solution (2×2) can be applied by a simple generalization which leads to the global separation. Whether for the noisy or noiseless case, the results obtained prove the reliability and efficiency by applying this analytical solution to achieve the desired objective, in particular by comparing the proposed algorithm with the application of two other separation algorithms, one of which involves the application of optimization techniques

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Global convergence towards statistical independence for noisy mixtures of stationary and non-stationary signals

SMATTI

Arar

2022

Int. j. inf. tecnol.

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A new speech enhancement adaptive algorithm based on fullband–subband MSE switching

Cited by 9 publications

References 25 publications

[Retracted] English Audio Language Retrieval Based on Adaptive Speech‐Adjusting Algorithm

[Retracted] English Audio Language Retrieval Based on Adaptive Speech‐Adjusting Algorithm

Source recovery by analytical maximization of phase-shifted kurtosis for the mixtures of noisy and noiseless signals

Global convergence towards statistical independence for noisy mixtures of stationary and non-stationary signals

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