Cascade convolutional neural network‐long short‐term memory recurrent neural networks for automatic tonal and nontonal preclassification‐based Indian language identification

Bhanja, Chuya China; Laskar, Mohammad Azharuddin; Laskar, Rabul Hussain

doi:10.1111/exsy.12544

Cited by 3 publications

(1 citation statement)

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“…Homogeneous speech segments are labelled based on Kernel-Gram matrix by unsupervised segmentation algorithm. Tonal and non-tonal languages [3,7] are distinguished by LID systems. Deep learning neural network model [4,9,13,14,15,20] is developed to perform LID.…”

Section: Analysis Of Speech Uttered In Different Languagesmentioning

confidence: 99%

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

2023

Preprint

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Recognizing linguistic information from speech has found applications in interpretation of language in which the utterance is spoken and the system could be used as a translator to convert sentence spoken in one language into another language meaningfully. Real time implementation of language identification (LID) from speech requires the speech to be fed from the Raspberry Pi board used in the transmitter section and the Raspberry Pi board in the receiver section receives it and given to the system for identifying the language of the speech. This system requires the training phase in which two dimensional spectrogram features are derived from the training set of speeches and given to the CNN layered architecture for creating templates for languages. Testing phase involves the transmission of speech from the memory card of the Raspberry Pi board in transmitter system. Raspberry Pi board in the receiver receives it and given to the system in receiver section. Two dimensional spectrogram features are derived for test speech and given to the CNN templates and based on the similarity index, test language is interpreted. This system is implemented using spectrogram, Melspectrogram and ERB spectrogram as features and CNN for modeling and classification of languages. Validation error is 1.4%, 1.8% and 3% for spectrogram, Melspectrogram and ERB spectrogram based systems respectively and decision level fusion classifier gives 0.9% as validation error. This system can be implemented in hardware by using Raspberry Pi board. This automated real time multilingual language identification system would be useful in forensic department and defense sectors to identify the persons belonging to any region or speaking in any language.

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Section: Analysis Of Speech Uttered In Different Languagesmentioning

confidence: 99%

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

2023

Preprint

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Differential spectral amplitude modulation and its applications in rolling bearing fault diagnosis

Jiang

Zhang²,

Xiang³

et al. 2022

Measurement

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An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

Dey

Sahidullah

Saha

2022

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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Cascade convolutional neural network‐long short‐term memory recurrent neural networks for automatic tonal and nontonal preclassification‐based Indian language identification

Cited by 3 publications

References 41 publications

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

Real Time Raspberry Pi based System for Linguistic Content Recognition from Speech

Differential spectral amplitude modulation and its applications in rolling bearing fault diagnosis

An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

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