A Framework for Multilingual Text- Independent Speaker Identification System

Nidhyananthan, Sundaradhas Selva; Kumari, R. Shantha Selva

doi:10.3844/jcssp.2014.178.189

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The system performance was tested using a different length of segment. The experimental result showed that combination between DMFCC and MFCC features achieved better performance rate in comparison with using each one individually, where the Error Rate obtained for MFCC, DMFCC and (MFCC+ DMFCC) is 5.8%, 2.9% and 1.2% with MFCC respectively [21].…”

Section: Related Workmentioning

confidence: 94%

Speaker Identification based on Hybrid Feature Extraction Techniques

AbuAladas¹,

Akram²,

Shaban³

et al. 2019

ijacsa

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One of the most exciting areas of signal processing is speech processing; speech contains many features or characteristics that can discriminate the identity of the person. The human voice is considered one of the important biometric characteristics that can be used for person identification. This work is concerned with studying the effect of appropriate extracted features from various levels of discrete wavelet transformation (DWT) and the concatenation of two techniques (discrete wavelet and curvelet transform) and study the effect of reducing the number of features by using principal component analysis (PCA) on speaker identification. Backpropagation (BP) neural network was also introduced as a classifier.

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Section: Related Workmentioning

confidence: 94%

Speaker Identification based on Hybrid Feature Extraction Techniques

AbuAladas¹,

Akram²,

Shaban³

et al. 2019

ijacsa

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show abstract

“…Also, these models have the ability to form a smooth approximation to the arbitrarily-shaped observation densities in the absence of other information (Nidhyananthan and Kumari, 2013). With Gaussian mixture models, each sound is modeled as a mixture of several Gaussian clusters in the feature space.…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

Speech/Music Classification Using Wavelet Based Feature Extraction Techniques

Ramalingam¹,

Dhanalakshmi²

2014

Journal of Computer Science

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Audio classification serves as the fundamental step towards the rapid growth in audio data volume. Due to the increasing size of the multimedia sources speech and music classification is one of the most important issues for multimedia information retrieval. In this work a speech/music discrimination system is developed which utilizes the Discrete Wavelet Transform (DWT) as the acoustic feature. Multi resolution analysis is the most significant statistical way to extract the features from the input signal and in this study, a method is deployed to model the extracted wavelet feature. Support Vector Machines (SVM) are based on the principle of structural risk minimization. SVM is applied to classify audio into their classes namely speech and music, by learning from training data. Then the proposed method extends the application of Gaussian Mixture Models (GMM) to estimate the probability density function using maximum likelihood decision methods. The system shows significant results with an accuracy of 94.5%.

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“…Recently, ELMs have been widely used in fields such as computer vision, biomedical engineering, and control and robotics, because they are simple, efficient and have impressive performance [18], [19] [20] and [21]. ELMs have single layer hidden node parameters which are randomly generated.…”

Section: B Identifying Speakers Using Extreme Learning Machinementioning

confidence: 99%

Comparison of I-vector and GMM-UBM approaches to speaker identification with TIMIT and NIST 2008 databases in challenging environments

Al-Kaltakchi

Woo

Dlay

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, two models, the I-vector and the Gaussian Mixture Model-Universal Background Model (GMM-UBM), are compared for the speaker identification task. Four feature combinations of I-vectors with seven fusion techniques are considered: maximum, mean, weighted sum, cumulative, interleaving and concatenated for both two and four features. In addition, an Extreme Learning Machine (ELM) is exploited to identify speakers, and then Speaker Identification Accuracy (SIA) is calculated. Both systems are evaluated for 120 speakers from the TIMIT and NIST 2008 databases for clean speech. Furthermore, a comprehensive evaluation is made under Additive White Gaussian Noise (AWGN) conditions and with three types of Non Stationary Noise (NSN), both with and without handset effects for the TIMIT database. The results show that the Ivector approach is better than the GMM-UBM for both clean and AWGN conditions without a handset. However, the GMM-UBM had better accuracy for NSN types.

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A Framework for Multilingual Text- Independent Speaker Identification System

Abstract: ABSTRACT

Cited by 6 publications

References 15 publications

Speaker Identification based on Hybrid Feature Extraction Techniques

Speaker Identification based on Hybrid Feature Extraction Techniques

Speech/Music Classification Using Wavelet Based Feature Extraction Techniques

Comparison of I-vector and GMM-UBM approaches to speaker identification with TIMIT and NIST 2008 databases in challenging environments

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