An engineering geology evaluation method based on an artificial neural network and its application

Xia, Yuanyou; Xie, Yan-Ming; Ruigeng, Zhu

doi:10.1016/s0013-7952(97)00015-x

Cited by 48 publications

(21 citation statements)

References 2 publications

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“…For identification, the proposed score-based strategy has been compared with the Back Propagation Neural Network (BPNN) [19] and the Gaussian Mixture Model (GMM) [20]. GMM is a robust model for text-independent speaker identification as reported in [20].…”

Section: Vocal Tract Responsementioning

confidence: 99%

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Text-Independent Speaker Identification Using Vowel Formants

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Aggoun

Amira

2015

J Sign Process Syst

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Automatic speaker identification has become a challenging research problem due to its wide variety of applications. Neural networks and audio-visual identification systems can be very powerful, but they have limitations related to the number of speakers. The performance drops gradually as more and more users are registered with the system. This paper proposes a scalable algorithm for real-time text-independent speaker identification based on vowel recognition. Vowel formants are unique across different speakers and reflect the vocal tract information of a particular speaker. The contribution of this paper is the design of a scalable system based on vowel formant filters and a scoring scheme for classification of an unseen instance. Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) have both been analysed for comparison to extract vowel formants by windowing the given signal. All formants are filtered by known formant frequencies to separate the vowel formants for further processing. The formant frequencies of each speaker are collected during the training phase. A test signal is also processed in the same way to find vowel formants and compare them with the saved vowel formants to identify the speaker for the current signal. A score-based scheme allows the speaker with the highest matching formants to own the current signal. This model requires less than 100 bytes of data to be saved for each speaker to be identified, and can identify the speaker within a second. Tests conducted on multiple databases show that this score-based scheme outperforms the back propagation neural network and Gaussian mixture models. Usually, the longer the speech files, the more significant were the improvements in accuracy.

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Section: Vocal Tract Responsementioning

confidence: 99%

“…One of the most common classes of neural networks is the feed-forward network [19]. Back propagation refers to a common method by which these networks can be trained.…”

Section: Vocal Tract Responsementioning

confidence: 99%

Text-Independent Speaker Identification Using Vowel Formants

Almaadeed

Aggoun

Amira

2015

J Sign Process Syst

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“…p.330 Ce problème de la conservation du corps prit une place prépondérante et encombra le Taoïsme de pratiques innombrables, minutieuses, fastidieuses, qui finirent par rebuter les meilleurs esprits, rejetant les plus positifs au Confucianisme, et les plus religieux au Bouddhisme. * * * p. 333 Le poète chinois dont je vais vous parler, Xi Kang, est plus connu encore dans son pays pour avoir été la figure principale du célèbre Club des Sept Sages de la Forêt de Bambous, Zhulin qixian, que pour ses oeuvres en grande partie perdues. Celles-ci n'en sont pas moins intéressantes, ne fût -ce que par leur style plus simple et moins maniéré, surtout moins pédant que celui de la plupart des écrivains de son temps.…”

Section: IIIunclassified

taoïsme

Maspero¹

2005

Classiques Des Sciences Sociales.

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“…In recent years, estimation models obtained from statistical and artificial neural networks methods widely used in geotechnical engineering instead of performing difficult laboratory and field tests (Kahraman et al, 2007;Karakus et al, 2004;Yasar and Erdogan, 2004;Najjar et al, 1996;Lee and Lee, 1996;Yuanyou et al, 1997;Yang and Zhang, 1998;Hurtado et al, 2001;Rafiq et al, 2001;Lee et al, 2003;Basma and Kallas, 2004;Gunaydın, 2008;Taskiran, 2010). Similar models were developed and evaluated for estimating of deformation modulus and coefficient of sub-grade reaction of compacted soils.…”

Section: Introductionmentioning

confidence: 99%