Phoneme analysis for multiple languages with fuzzy-based speaker identification

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IET Biometrics Pub Date : 2022-07-26 DOI:10.1049/bme2.12078

Thales Aguiar de Lima, Márjory Cristiany Da-Costa Abreu

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Abstract

Most voice biometric systems are dependent on the language of the user. However, if the idea is to create an all-inclusive and reliable system that uses speech as its input, then they should be able to recognise people regardless of language or accent. Thus, this paper investigates the effects of languages on speaker identification systems and the phonetic impact on their performance. The experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. The Mel-Frequency Cepstrum Coefficients and its Deltas are extracted from those languages. Also, this paper expands the research study of fuzzy models in the speaker recognition field, using a Fuzzy C-Means and Fuzzy k-Nearest Neighbours and comparing them with k-Nearest Neighbours and Support Vector Machines. Results with more languages decreases the accuracy from 92% to 85.59%, but further investigation suggests it is caused by the number of classes. A phonetic investigation finds no relation between the phonemes and the results. Finally, fuzzy methods offer more flexibility and in some cases, even better results compared to their crisp version. Therefore, the biometric system presented here is not affected by multilingual environments.

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基于模糊说话人识别的多语言音素分析

大多数语音生物识别系统都依赖于用户的语言。然而，如果我们的想法是创建一个全面可靠的系统，使用语音作为输入，那么他们应该能够识别出任何语言或口音的人。因此，本文研究了语言对说话人识别系统的影响，以及语音对系统性能的影响。实验使用三种广泛使用的语言进行，即葡萄牙语、英语和汉语。从这些语言中提取了Mel-Frequency倒频谱系数及其δ。同时，本文扩展了模糊模型在说话人识别领域的研究，使用了模糊c均值和模糊k近邻，并将它们与k近邻和支持向量机进行了比较。结果表明，语言数越多，准确率从92%下降到85.59%，但进一步研究表明，这是由类别数量造成的。语音调查发现音素和结果之间没有关系。最后，模糊方法提供了更多的灵活性，在某些情况下，甚至比他们的清晰版本更好的结果。因此，这里介绍的生物识别系统不受多语言环境的影响。

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来源期刊

IET Biometrics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

5.90

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： The field of biometric recognition - automated recognition of individuals based on their behavioural and biological characteristics - has now reached a level of maturity where viable practical applications are both possible and increasingly available. The biometrics field is characterised especially by its interdisciplinarity since, while focused primarily around a strong technological base, effective system design and implementation often requires a broad range of skills encompassing, for example, human factors, data security and database technologies, psychological and physiological awareness, and so on. Also, the technology focus itself embraces diversity, since the engineering of effective biometric systems requires integration of image analysis, pattern recognition, sensor technology, database engineering, security design and many other strands of understanding. The scope of the journal is intentionally relatively wide. While focusing on core technological issues, it is recognised that these may be inherently diverse and in many cases may cross traditional disciplinary boundaries. The scope of the journal will therefore include any topics where it can be shown that a paper can increase our understanding of biometric systems, signal future developments and applications for biometrics, or promote greater practical uptake for relevant technologies: Development and enhancement of individual biometric modalities including the established and traditional modalities (e.g. face, fingerprint, iris, signature and handwriting recognition) and also newer or emerging modalities (gait, ear-shape, neurological patterns, etc.) Multibiometrics, theoretical and practical issues, implementation of practical systems, multiclassifier and multimodal approaches Soft biometrics and information fusion for identification, verification and trait prediction Human factors and the human-computer interface issues for biometric systems, exception handling strategies Template construction and template management, ageing factors and their impact on biometric systems Usability and user-oriented design, psychological and physiological principles and system integration Sensors and sensor technologies for biometric processing Database technologies to support biometric systems Implementation of biometric systems, security engineering implications, smartcard and associated technologies in implementation, implementation platforms, system design and performance evaluation Trust and privacy issues, security of biometric systems and supporting technological solutions, biometric template protection Biometric cryptosystems, security and biometrics-linked encryption Links with forensic processing and cross-disciplinary commonalities Core underpinning technologies (e.g. image analysis, pattern recognition, computer vision, signal processing, etc.), where the specific relevance to biometric processing can be demonstrated Applications and application-led considerations Position papers on technology or on the industrial context of biometric system development Adoption and promotion of standards in biometrics, improving technology acceptance, deployment and interoperability, avoiding cross-cultural and cross-sector restrictions Relevant ethical and social issues