Automatic language identification for seven Indian languages using higher level features

Madhu, Chithra; George, Anu; Mary, Leena

doi:10.1109/spices.2017.8091332

Cited by 26 publications

(8 citation statements)

References 9 publications

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“…In [100], LID task was performed with seven Indian languages; Assamese, Bengali, Hindi, Manipuri, Punjabi, Telugu, Urdu. The authors developed two LID systems based on phonotactic and prosodic features, respectively.…”

Section: Literature Review Of Relatively Recent Research Work For Ind...mentioning

confidence: 99%

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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Section: Literature Review Of Relatively Recent Research Work For Ind...mentioning

confidence: 99%

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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“…Accent recognition is similar to language identification [4,5,6] and speaker identification [7,8,9]. They all classify variable-length speech sequences to utterance-level posteriors to obtain accent, speaker or language ID.…”

Section: Related Workmentioning

confidence: 99%

The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods

Shi

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The variety of accents has posed a big challenge to speech recognition. The Accented English Speech Recognition Challenge (AESRC2020) is designed for providing a common testbed and promoting accent-related research. Two tracks are set in the challenge -English accent recognition (track 1) and accented English speech recognition (track 2). A set of 160 hours of accented English speech collected from 8 countries is released with labels as the training set. Another 20 hours of speech without labels is later released as the test set, including two unseen accents from another two countries used to test the model generalization ability in track 2. We also provide baseline systems for the participants. This paper first reviews the released dataset, track setups, baselines and then summarizes the challenge results and major techniques used in the submissions.

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“…Accent recognition is similar to language identification [5,6,7] and speaker identification [8,9,10,11]. They all classify variable-length speech sequences to utterance-level posteriors to obtain accent, speaker or language ID.…”

Section: Related Workmentioning

confidence: 99%

The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods

Shi

et al. 2021

Preprint

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Automatic language identification for seven Indian languages using higher level features

Cited by 26 publications

References 9 publications

An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods

The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods

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