IIITH-ILSC Speech Database for Indain Language Identification

Vuddagiri, Ravi Kumar; Gurugubelli, Krishna; Jain, Priyam; Vydana, Hari Krishna; Vuppala, Anil Kumar

doi:10.21437/sltu.2018-12

Cited by 14 publications

(9 citation statements)

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“…3, the comparative LID performance of four acoustic features are shown in terms of detection error tradeof (DET) curve and equal error rate (EER) (see Section 2.3). Eight of the most widely spoken Indian languages, Hindi, Bengali, Marathi, Telugu, Tamil, Gujarati, Urdu, and Punjabi, are selected from the IIITH-ILSC database [191]. For all the acoustic features, 13-dimensional static cepstral coeicients are used.…”

Section: Description Of Diferent Language Discriminating Features Pre...mentioning

confidence: 99%

“…4, we have shown the language discriminating capabilities of several intonation, duration, and energybased prosody features. We have selected ive Indian languages, Bengali, Hindi, Punjabi, Tamil, and Urdu, from the IIITH-ILSC database [191]. For each of the languages, we have computed the mean prosodic feature values across the utterances.…”

Section: Prosodic Featuresmentioning

confidence: 99%

“…For that reason, we have attempted to plot the phoneme occurrence likelihoods of several Indian languages by categorizing the phonemes into several sub-classes. We have used 12 Indian languages from the IIITH-ILSC [191] corpus. The phonemes are categorized into six sub-classes according to the manner and place of articulation [186].…”

Section: Challenges Of Mutual Influence and Similaritymentioning

confidence: 99%

“…. In a similar spirit, recently, another multilingual speech corpus IIITH-ILSC [191], consisting of 23 Indian languages, was introduced. It contains all the 22 oicial languages of Indian along with Indian English.…”

Section: Iiith-ilscmentioning

confidence: 99%

“…In Table 3, we have summarized the key information about the major speech corpora developed for Indian spoken language recognition research. IIITH-ILSC [191] VoxLingua107 [185] (Indian languages only) NITS-LD [24] EMILLE/ CIIL [201] No. of languages [116] used parallel phone recognizer (PPR) based language modeling followed by SVM for language classiication.…”

Section: Other Developmentsmentioning

confidence: 99%

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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: Description Of Diferent Language Discriminating Features Pre...mentioning

confidence: 99%

Section: Prosodic Featuresmentioning

confidence: 99%