Automatic Speech Recognition System for Tonal Languages: State-of-the-Art Survey

Kaur, Jaspreet; Singh, Amitoj; Kadyan, Virender

doi:10.1007/s11831-020-09414-4

Cited by 44 publications

(18 citation statements)

References 87 publications

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“…The purpose of this systematic survey is, to sum up, the best available research on automatic speech recognition of Indian languages. Kaur et al [147] reviewed the status of speech recognition research conducted on tonal languages spoken around the globe. Authors observed that a lot of work has been done for the Asian continent tonal languages, i.e., Chinese, Thai, Vietnamese, Mandarin, but little work been reported for the Mizo, Bodo, Indo-European tonal languages like Punjabi, Latvian, Lithuanian as well for the African continental tonal languages, i.e., Hausa and Yoruba (Tables 1, 2).…”

Section: Inclusion/exclusion Criteriamentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

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Speech recognition of a language is a key area in the field of pattern recognition. This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics. An immense number of frameworks are available for speech processing and recognition for languages persisting around the globe. However, a limited number of automatic speech recognition systems are available for commercial use. The gap between the languages being spoken around the globe and the technical support available to these languages are very few. This paper examined major challenges for speech recognition for different languages. Analysis of the literature shows that lack of standard databases availability of minority languages hinder the research recognition research across the globe. When compared with non-Indian languages, the research on speech recognition of Indian languages (except Hindi) has not achieved the expected milestone yet. Combination of MFCC and DNN–HMM classifier is most commonly used system for developing ASR minority languages, whereas in some of the majority languages, researchers are using much advance algorithms of DNN. It has also been observed that the research in this field is quite thin and still more research needs to be carried out, particularly in the case of minority languages.

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Section: Inclusion/exclusion Criteriamentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

Self Cite

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“…Alternatively, biometrics, recognising individuals from behavior and biological characteristics, 6 gained attention for authentication. This is attributed to biometric sensors included in smartphones, such as fingerprint [139] (pore and ridge structure [256]), voice [48] (mel frequency cepstral coding, today deep neural networks [144]), gait (heel-strike ratio [237] or cycle matching [200]), face (features learned in deep neural networks [134]), keystroke dynamics (key-press latencies [195]), or iris [290] (image intensity maps from Hough-transformed Daugman rubber sheet models [281]).…”

Section: Limitations Of Traditional Authentication Schemesmentioning

confidence: 99%

Adversary Models for Mobile Device Authentication

Mayrhofer

Sigg

2021

ACM Comput. Surv.

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Mobile device authentication has been a highly active research topic for over 10 years, with a vast range of methods proposed and analyzed. In related areas, such as secure channel protocols, remote authentication, or desktop user authentication, strong, systematic, and increasingly formal threat models have been established and are used to qualitatively compare different methods. However, the analysis of mobile device authentication is often based on weak adversary models, suggesting overly optimistic results on their respective security. In this article, we introduce a new classification of adversaries to better analyze and compare mobile device authentication methods. We apply this classification to a systematic literature survey. The survey shows that security is still an afterthought and that most proposed protocols lack a comprehensive security analysis. The proposed classification of adversaries provides a strong and practical adversary model that offers a comparable and transparent classification of security properties in mobile device authentication.

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“…Many systems treat pitch (the main phonetic cue of tone) as a completely separate feature. In such systems, the traditional ASR algorithm learns the segments, and a separate machine learning module learns the pitch patterns and offers its inference of the tone (Kaur et al, 2020). This has been used for languages like Mandarin (Niu et al, 2013;Shan et al, 2010), Thai (Kertkeidkachorn et al, 2014) and Yoruba (O .…”

Section: Tonal Languages and Asrmentioning

confidence: 99%

Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

Mager¹,

Oncevay²,

Rios³

et al. 2021

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This area is in all probability unmatched, anywhere in the world, in its linguistic multiplicity and diversity. A couple of thousand languages and dialects, at present divided into 17 large families and 38 small ones, with several hundred unclassified single languages, are on record. In one small portion of the area, in Mexico just north of the Isthmus of Tehuantepec, one finds a diversity of linguistic type hard to match on an entire continent in the Old World.

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Automatic Speech Recognition System for Tonal Languages: State-of-the-Art Survey

Cited by 44 publications

References 87 publications

Computational intelligence in processing of speech acoustics: a survey

Computational intelligence in processing of speech acoustics: a survey

Adversary Models for Mobile Device Authentication

Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

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