Speaker Verification Using Boosted Cepstral Features with Gaussian Distributions

Ahmad, Sahrim; Muhammad, E.; Khurshid, Khawar

doi:10.1109/inmic.2007.4557681

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…The speaker-specific information can be extracted from feature extraction techniques at a reduced data rate [13]. These feature vectors contain vocal tract, excitation source and behavioral traits of speaker-specific information [4].…”

Section: Speaker Verification Studies Using Different Featuresmentioning

confidence: 99%

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Limited Data Speaker Verification: Fusion of Features

Kumari¹,

Jayanna²

2017

IJECE

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<p>The present work demonstrates experimental evaluation of speaker verification for different speech feature extraction techniques with the constraints of limited data (less than 15 seconds). The state-of-the-art speaker verification techniques provide good performance for sufficient data (greater than 1 minutes). It is a challenging task to develop techniques which perform well for speaker verification under limited data condition. In this work different features like Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPCC), Delta (4), Delta-Delta (44), Linear Prediction Residual (LPR) and Linear Prediction Residual Phase (LPRP) are considered. The performance of individual features is studied and for better verification performance, combination of these features is attempted. A comparative study is made between Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM) through experimental evaluation. The experiments are conducted using NIST-2003 database. The experimental results show that, the combination of features provides better performance compared to the individual features. Further GMM-UBM modeling gives reduced equal error rate (EER) as compared to GMM.</p>

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Section: Speaker Verification Studies Using Different Featuresmentioning

confidence: 99%

“…The simplest approach to train a UBM is to pool all the data and use it via expectation-maximization (EM) algorithm [20]. The coupled target and background speaker model components are integrated effectively while performing speaker recognition, when Maximum a posteriori (MAP) adaptation is used [13].…”

Section: Speaker Modeling and Testingmentioning

confidence: 99%

Limited Data Speaker Verification: Fusion of Features

Kumari¹,

Jayanna²

2017

IJECE

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“…However, linear prediction model is a pure mathematics model without consideration on processing features of human auditory system. Other techniques used are: Linear Predictive Cepstral Coefficients (LPCC); Perceptual Linear Prediction (PLP); Mel-Frequency Cepstral Coefficients (MFCC); and Neural Predictive Coding (NPC) [1], [2], [3]. MFCC is a popular technique because it is based on the known variation of the human ear's critical frequency bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Isolated Word Speech Recognition System Based On FPGA

Hu¹,

Zhang²,

Zhan³

et al. 2013

JCP

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The paper introduces an isolated word speech recognition system in which the speech signal is acquired in real time. Half Raised-Sine function is applied to the MFCC parameters of the audio files, and improved DTW algorithm is implemented. Simulation results show that compared the conventional DTW with the improved DTW algorithm, the latter can obtain a better recognition rate and faster response time. Finally, the system was implemented on FPGA and yields satisfactory performance.

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“…A wide range of approaches may be used to parametrically represent the speech signal to be used in the SR activity (Saeidi et al 2007;Zilca et al 2003). Some of the techniques include: Linear Prediction Coding (LPC); Mel-Frequency Cepstral Coefficients (MFCC); Linear Predictive Cepstral Coefficients (LPCC); Perceptual Linear Prediction (PLP); and Neural Predictive Coding (NPC) (Salman et al 2007;Paul et al 2009;Charbuillet et al 2007). MFCC is a popular technique because it is based on the known variation of the human ear's critical frequency bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Speaker recognition utilizing distributed DCT-II based Mel frequency cepstral coefficients and fuzzy vector quantization

Hossan

Gregory

2012

Int J Speech Technol

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In this paper, a new and novel Automatic Speaker Recognition (ASR) system is presented. The new ASR system includes novel feature extraction and vector classification steps utilizing distributed Discrete Cosine Transform (DCT-II) based Mel Frequency Cepstral Coefficients (MFCC) and Fuzzy Vector Quantization (FVQ). The ASR algorithm utilizes an approach based on MFCC to identify dynamic features that are used for Speaker Recognition (SR). A series of experiments were performed utilizing three different feature extraction methods: (1) conventional MFCC; (2) Delta-Delta MFCC (DDMFCC); and (3) DCT-II based DDMFCC. The experiments were then expanded to include four classifiers: (1) FVQ; (2) K-means Vector Quantization (VQ); (3) Linde, Buzo and Gray VQ; and (4) Gaussian Mixed Model (GMM). The combination of DCT-II based MFCC, DMFCC and DDMFCC with FVQ was found to have the lowest Equal Error Rate for the VQ based classifiers. The results found were an improvement over previously reported non-GMM methods and approached the results achieved for the computationally expensive GMM based method. Speaker verification tests carried out highlighted the overall performance improvement for the new ASR system. The National Institute of Standards and Technology Speaker Recognition Evaluation corpora was used to provide speaker source data for the experiments.

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Speaker Verification Using Boosted Cepstral Features with Gaussian Distributions

Cited by 11 publications

References 6 publications

Limited Data Speaker Verification: Fusion of Features

Limited Data Speaker Verification: Fusion of Features

Isolated Word Speech Recognition System Based On FPGA

Speaker recognition utilizing distributed DCT-II based Mel frequency cepstral coefficients and fuzzy vector quantization

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