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- W4361018049 abstract "Designing a local language communication system that enables regular people to interact verbally with machines to access information or perform day-to-day operations is a major challenge for India. India is a multilingual country as there are many spoken languages. This paper presents comprehensive literature and a systematic review of low-resource languages like Indian languages for spoken language identification. It has been noticed that conventional feature extraction techniques in ASR systems such as Mel frequency cepstral coefficient (MFCC), and RASTA perceptual linear prediction (RASTA-PLP), perform poorly in loud surroundings. This paper offers an in-depth examination of numerous studies that have been conducted since 2015. The analysis and investigation were performed using the pertinent data from 50 research studies papers selected after the screening process between the years 2015 to 2022. From the research, it has been observed that due to the noisy environment accuracy of speech signal varies with different feature extraction techniques. During this study, it has been seen that LSTM speech signals in a noisy environment show higher precision as compared to other conventional techniques of feature extraction. By critical review of the various papers related to the topic, it was observed that not much work had been done on Long short-term memory (LSTM) and Bidirectional Long short term memory (Bi-LSTM) for low-resource languages like Hindi. By selecting an appropriate feature extraction technique in speech recognition (SR), the results of this review can be utilized to improve voice recognition technology/spoken language identification for noisy environments." @default.
- W4361018049 created "2023-03-30" @default.
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- W4361018049 date "2023-01-01" @default.
- W4361018049 modified "2023-10-17" @default.
- W4361018049 title "Comprehensive and Systematic Review of Various Feature Extraction Techniques for Vernacular Languages" @default.
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- W4361018049 doi "https://doi.org/10.1007/978-3-031-27499-2_33" @default.
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