Performance Evaluation of Language Identification on Emotional Speech Corpus of Three Indian Languages

Basu, Joyanta; Majumder, Swanirbhar

doi:10.1007/978-981-15-9290-4_6

Cited by 2 publications

(3 citation statements)

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“…Basu et al (2021) [17] evaluated the performance of Indian LID systems on emotional speech. The data was collected from three Indian languages; Assamese, Bengali, and Santali.…”

Section: Das Et Al (2020)mentioning

confidence: 99%

“…Both of these corpora covered more than 20 major Indian languages. A notable amount of the research works [1,17,44,122,159] are conducted with self-collected in-house corpora. • The availability of speech data for each Indian language has increased with time.…”

Section: Summary Of the Reviewed Research Workmentioning

confidence: 99%

“…LID research was primarily conined to privately developed, standardized corpora. Recently, in [6], LID system was developed with three open-source corpora; Mozilla Common Voice [5], Google Bengali speech corpus, 16 and Audio Lingua, 17 which encouraged using open-sourced speech data for LID research. Open source corpora can be developed for the Indian languages by crowd-sourcing or collecting data from the web.…”

Section: Issue Of Low-resourcementioning

confidence: 99%

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An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

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ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.

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“…Basu et al (2021) [17] evaluated the performance of Indian LID systems on emotional speech. The data was collected from three Indian languages; Assamese, Bengali, and Santali.…”

Section: Das Et Al (2020)mentioning

confidence: 99%