Speaker identification for security systems using reinforcement-trained pRAM neural network architectures

Clarkson, T.G.; Christodoulou, Chris; Guan, Y.; Gorse, Denise; Romano-Critchley, David; Taylor, John G.

doi:10.1109/5326.923269

Cited by 24 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is highly flexible and convenient for a wide range of daily-life applications. Various approaches, involving neural networks (Clarkson et al, 2001), Gaussian mixture models (GMMs) (Burget et al, 2007), and support vector machines (SVMs) (Cortes et al, 1995), have been adopted for recognizing speakers. Among them, SVM-based speaker recognition has recently attracted much attention.…”

Section: Embedded System Design For Speaker Identification/verificationmentioning

confidence: 99%

Design and Applications of Embedded Systems for Speech Processing

Wang¹,

Lin²,

Chen³

2012

Embedded Systems - High Performance Systems, Applications and Projects

View full text Add to dashboard Cite

Section: Embedded System Design For Speaker Identification/verificationmentioning

confidence: 99%

Design and Applications of Embedded Systems for Speech Processing

Wang¹,

Lin²,

Chen³

2012

Embedded Systems - High Performance Systems, Applications and Projects

View full text Add to dashboard Cite

“…In order to model the statistical variations, the hidden Markov model (HMM) for textdependent speaker recognition was studied. The system performances in neural network based networks were also studied (Clarkson et al, 2006). In HMM, time-dependent parameters are observation symbols.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In 1995, Reynolds proposed Gaussian mixture modeling (GMM) classifier for speaker recognition task (Krause and Gazit, 2006;Clarkson et al, 2006). This is the most widely used probabilistic technique in speaker recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A New Speaker Recognition System with Combined Feature Extraction Techniques

Sumithra¹

2011

Journal of Computer Science

View full text Add to dashboard Cite

Problem statement:This study introduces a new method for speaker verification system by fusing two different feature extraction methods to improve the recognition accuracy and security. Approach: The proposed system uses Mel frequency cepstral coefficients for speaker identification and Modified MFCC for verification. For speaker modeling vector quantization is used. Results: The proposed system was investigated the effect of the different length segmental feature as well as speaker modeling for speaker recognition. The performance was evaluated against 1000 speakers for 10 different languages with duration of 10 sec for training the system and for testing 5 sec. duration samples were used. Conclusion/Recommendations: Experimental results of the proposed system showed that higher recognition accuracy of 93% is achieved by increasing the number of filter banks used for feature extraction method, more competitive with existing system using vector quantization with lesser computational complexity. The system efficiency may further be improved using other speaker modeling techniques like GMM, HMM.

show abstract

“…Recently, speaker recognition system has many applications in real-world. Various technologies such as neural network [2], GMM [3], and SVM [4] have also been adopted for it. Among them, the SVM [5] based speaker recognition has attracted much attention recently.…”

Section: Introductionmentioning

confidence: 99%

Hardware/software co-design for fast-trainable speaker identification system based on SMO

Wang

Peng

Wang

et al. 2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

View full text Add to dashboard Cite

Embedded speaker identification system is a popular research, but most of current systems can not provide fast training ability. Because of the low computational ability in the embedded environment, a large amount of waiting time usually makes the human-machine interface not friendly. This paper presents a hardware and software (HW/SW) co-design solution for fast-trainable speaker identification system. Fast training ability makes this embedded speaker identification system possess high flexibility and enhances the convenience to a wide range of real-world applications. The proposed system consists of a training phase and a multiclass identification phase. The sequential minimal optimization (SMO) training algorithm occupies the heaviest computational load and is realized as a dedicated VLSI module, i.e., the hardware component. The other processes such as speech preprocess, speech feature extraction, and SVM voting strategy are implemented by software. Moreover, a data-packed mechanism is presented to improve the bandwidth utilization. Compared with the embedded C code based on ARM processor, our system reduces 90% of the training time and achieves 89.9% identification rate with the NIST 2010 speaker recognition database. The proposed system was tested and found to be fully functional working on a Socle CDK prototype system with an AMBA based Xilinx FPGA and an ARM926EJ processor.

show abstract

Speaker identification for security systems using reinforcement-trained pRAM neural network architectures

Cited by 24 publications

References 12 publications

Design and Applications of Embedded Systems for Speech Processing

Design and Applications of Embedded Systems for Speech Processing

A New Speaker Recognition System with Combined Feature Extraction Techniques

Hardware/software co-design for fast-trainable speaker identification system based on SMO

Contact Info

Product

Resources

About