Accuracy of MFCC-Based Speaker Recognition in Series 60 Device

Saastamoinen, Juhani; Karpov, Evgeny; Hautamäki, Ville; Fränti, Pasi

doi:10.1155/asp.2005.2816

Cited by 14 publications

(11 citation statements)

References 10 publications

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“…Despite its secondary role, VQ gives comparable accuracy to GMM [6,46] when equipped with a MAP adaptation [32]. The computational benefits over GMM are important in small-footprint implementations such as mobile devices [71]. Recently, similar to hybrids of GMM and SVM [11], combination of VQ with SVM has also been studied [6].…”

Section: Review Of Clustering Methods In Speaker Recognitionmentioning

confidence: 99%

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Comparison of clustering methods: A case study of text-independent speaker modeling

Kinnunen¹,

Sidoroff²,

Tuononen³

et al. 2011

Pattern Recognition Letters

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Clustering is needed in various applications such as biometric person authentication, speech coding and recognition, image compression and information retrieval. Hundreds of clustering methods have been proposed for the task in various fields but, surprisingly, there are few extensive studies actually comparing them. An important question is how much the choice of a clustering method matters for the final pattern recognition application. Our goal is to provide a thorough experimental comparison of clustering methods for text-independent speaker verification. We consider parametric Gaussian mixture model (GMM) and non-parametric vector quantization (VQ) model using the best known clustering algorithms including iterative (K-means, random swap, expectation-maximization), hierarchical (pairwise nearest neighbor, split, split-and-merge), evolutionary (genetic algorithm), neural (self-organizing map) and fuzzy (fuzzy C-means) approaches. We study recognition accuracy, processing time, clustering validity, and correlation of clustering quality and recognition accuracy. Experiments from these complementary observations indicate clustering is not a critical task in speaker recognition and the choice of the algorithm should be based on computational complexity and simplicity of the implementation. This is mainly because of three reasons: the data is not clustered, large models are used and only the best algorithms are considered. For low-order models, choice of the algorithm, however, can have a significant effect. Index Terms

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Section: Review Of Clustering Methods In Speaker Recognitionmentioning

confidence: 99%

“…First of all, in our experience double accuracy should always be used with the EM algorithm. In contrast, standard K-means can be implemented even with fixed-point arithmetic on a hand-held device [71]. However, in GMM, the covariance matrices can become singular (or non-invertible).…”

Section: Implementation Considerationsmentioning

confidence: 99%

Comparison of clustering methods: A case study of text-independent speaker modeling

Kinnunen¹,

Sidoroff²,

Tuononen³

et al. 2011

Pattern Recognition Letters

Self Cite

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“…It was introduced to speaker recognition in the 1980s [32,213] and its roots are originally in data compression [73]. Even though VQ is often used for computational speedup techniques [142,120,199] and lightweight practical implementations [202], it also provides competitive accuracy when combined with background model adaptation [88,124]. We will return to adaptation methods in Subsection 4.2.…”

Section: Vector Quantizationmentioning

confidence: 99%

An overview of text-independent speaker recognition: From features to supervectors

Kinnunen¹,

Li²

2010

Speech Communication

1,224

706

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This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.

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“…The estimated value of parameters x and y is calculated by three samples for scoring. Such systematic scoring and expert scoring have a high degree of similarity, which makes the system more accurate and valuable for scoring of spoken pronunciation [11].…”

Section: Pronunciation Feedback Evaluation Methods and Technologiesmentioning

confidence: 99%

Design of Students’ Spoken English Pronunciation Training System Based on Computer VB Platform

Jin

2019

Int. J. Emerg. Technol. Learn.

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Spoken English communication is most commonly used in the international communication. However, the accuracy of spoken English pronunciation is the key factor to restrict English learners in China. For the current situation that spoken English proficiency is generally low in China, this paper aims to design a spoken English pronunciation training system that will provide guidance and help for English learners’ spoken pronunciation. The Visual Basic platform is used in the design of the system. This paper first conducts an in-depth study on the related theories of voice recognition, discusses the correction algorithm of voice scoring and pronunciation, and puts forward more practical and convenient AP-based scoring method, providing full theoretical support for the design of the system. Then through the function analysis and design of the spoken English pronunciation training system, this paper realizes the system design of scoring and correcting errors of English spoken pronunciation based on the VB platform. The system boasts the basic functions, including English phonetic symbols and word pronunciation to follow, real-time voice evaluation, and pronunciation error correction. According to the test, the similarity of the system with the experts is over 90% in scoring and its efficiency of pronunciation error correction reaches 80%, which plays a certain role in improving spoken English of English learners.

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Accuracy of MFCC-Based Speaker Recognition in Series 60 Device

Cited by 14 publications

References 10 publications

Comparison of clustering methods: A case study of text-independent speaker modeling

Comparison of clustering methods: A case study of text-independent speaker modeling

An overview of text-independent speaker recognition: From features to supervectors

Design of Students’ Spoken English Pronunciation Training System Based on Computer VB Platform

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