Using SVM as Back-End Classifier for Language Identification

Suo, Hongbin; Li, Ming; Lü, Ping; Yan, Yonghong

doi:10.1155/2008/674859

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…Given an MFCC frame, the mixture component is found which produces the highest likelihood score, and the index of that component becomes the token for that frame (Figure 2). For a stream of input frames, a stream of component indices will be produced, on which language modelling followed by back-end classification can be performed, as is common in audio LID [15], [16]. For the NIST 1996 12 language evaluation task [17], [15] report a minimum error rate of 17%, which is higher than standard PRLM techniques.…”

Section: A Audio Language Identificationmentioning

confidence: 99%

“…9. Showing the mean error-rate across six different codebook sizes (8,16,32,64, 128 and 256 codes) for VLID systems trained on visual speech recorded in two different ways. The upper trace is the error when trained on three separate recitals of the UN declaration in English, spoken by the same speaker, and designed to be identical in rendition.…”

Section: Bias Due To Speaking Rate and Recording Conditionsmentioning

confidence: 99%

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Language Identification Using Visual Features

Newman

Cox

2012

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to identify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discriminating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech.

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Section: A Audio Language Identificationmentioning

confidence: 99%

Section: Bias Due To Speaking Rate and Recording Conditionsmentioning

confidence: 99%

Language Identification Using Visual Features

Newman

Cox

2012

IEEE Trans. Audio Speech Lang. Process.

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“…The best-achieved accuracy by the kernel polynomial function is 0.9979712 (99.8 %), which is the same as from the linear model. The next model, RBF (Gaussian) kernel, comes from a family of kernels where a distance measure is smoothed by an exponential function (Suo et al, 2008). RBF is the most used type of kernel function, mainly because it has a localised and finite response along the entire x-axis.…”

Section: Research Resultsmentioning

confidence: 99%

A Novel Data Mining Approach for Defect Detection in the Printed Circuit Board Manufacturing Process

Bártová

Bína

2022

Engineering Management in Production and Services

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This research aims to propose an effective model for the detection of defective Printed Circuit Boards (PCBs) in the output stage of the Surface-Mount Technology (SMT) line. The emphasis is placed on increasing the classification accuracy, reducing the algorithm training time, and a further improvement of the final product quality. This approach combines a feature extraction technique, the Principal Component Analysis (PCA), and a classification algorithm, the Support Vector Machine (SVM), with previously applied Automated Optical Inspection (AOI). Different types of SVM algorithms (linear, kernels and weighted) were tuned to get the best accuracy of the resulting algorithm for separating good-quality and defective products. A novel automated defect detection approach for the PCB manufacturing process is proposed. The data from the real PCB manufacturing process were used for this experimental study. The resulting PCALWSVM model achieved 100 % accuracy in the PCB defect detection task. This article proposes a potentially unique model for accurate defect detection in the PCB industry. A combination of PCA and LWSVM methods with AOI technology is an original and effective solution. The proposed model can be used in various manufacturing companies as a postprocessing step for an SMT line with AOI, either for accurate defect detection or for preventing false calls.

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“…Linear kernels are the simplest kernel functions, represented by the product x, y [23]. This kernel is the basic kernel that is most often used by SVM because with this kernel, SVM divides data linearly.…”

Section: Linear Kernelmentioning

confidence: 99%

“…The RBF kernel is a kernel family where distance measurements are smoothed by radials function (exponential function) [23]. The RBF kernel is denoted as in Equation (4),…”

Section: Polynomial Kernelmentioning

confidence: 99%

Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

et al. 2020

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Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

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Using SVM as Back-End Classifier for Language Identification

Cited by 11 publications

References 6 publications

Language Identification Using Visual Features

Language Identification Using Visual Features

A Novel Data Mining Approach for Defect Detection in the Printed Circuit Board Manufacturing Process

Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

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