DA-IICT/IIITV System for Low Resource Speech Recognition Challenge 2018

Sailor, Hardik B.; Krishna, Maddala V. Siva; Chhabra, Diksha; Patil, Ankur T.; Kamble, Madhu R.; Patil, Hemant A.

doi:10.21437/interspeech.2018-1553

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…The data sets are different. [52], [53], [49], and [55] used isolated Gujarati words, [54] used 25-word sentences, [56] did not limit to number of words in the sentences, [51], [57] used continuous speech of three Indian languages, and [50] used continuous speech of 9 Indian languages. The table highlights the accuracy achieved with Gujarati language.…”

Section: Mathematical Evaluation Of Resultsmentioning

confidence: 99%

See 1 more Smart Citation

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language using CatBoost

Gupta

Singh²,

Singh

2021

Preprint

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The pandemic caused due to COVID-19, has seen things going online. People tired of typing prefer to give voice commands. Most of the voice based applications and devices are not prepared to handle the native languages. Moreover, in a party environment it is difficult to identify a voice command as there are many speakers. The proposed work addresses the Cocktail party problem of Indian language, Gujarati. The voice response systems like, Siri, Alexa, Google Assistant as of now work on single voice command. The proposed algorithm G- Cocktail would help these applications to identify command given in Gujarati even from a mixed voice signal. Benchmark Dataset is taken from Microsoft and Linguistic Data Consortium for Indian Languages(LDC-IL) comprising single words and phrases. G-Cocktail utilizes the power of CatBoost algorithm to classify and identify the voice. Voice print of the entire sound files is created using Pitch, and Mel Frequency Cepstral Coefficients (MFCC). Seventy percent of the voice prints are used to train the network and thirty percent for testing. The proposed work is tested and compared with K-means, Naïve Bayes, and LightGBM.

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Section: Mathematical Evaluation Of Resultsmentioning

confidence: 99%

“…There is no historical evidence of Cocktail-party scene with Gujarati language [47][48][49][50][51][52][53][54][55][56][57]. For ASR in Gujarati, methods like Statistical, Neural Networks and End-to-end recognition are used [35].…”

Section: Introductionmentioning

confidence: 99%

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language using CatBoost

Gupta

Singh²,

Singh

2021

Preprint

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“…The DAIICT-IIITV Gujarati system [15] used a combination of TDNN and TDNN-LSTM Acoustic Models with various acoustic features. RNN-based Language Models for rescoring were found to outperform n-gram models.…”

Section: Resultsmentioning

confidence: 99%

Interspeech 2018 Low Resource Automatic Speech Recognition Challenge for Indian Languages

Srivastava¹,

Sitaram²,

Mehta³

et al. 2018

6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)

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India has more than 1500 1 languages, with 30 of them spoken by more than one million native speakers. Most of them are low-resource and could greatly benefit from speech and language technologies. Building speech recognition support for these low-resource languages requires innovation in handling constraints on data size, while also exploiting the unique properties and similarities among Indian languages. With this goal, we organized a low-resource Automatic Speech Recognition challenge for Indian languages as part of Interspeech 2018. We released 50 hours of speech data with transcriptions for Tamil, Telugu and Gujarati, amounting to a total of 150 hours. Participants were required to only use the data we released for the challenge to preserve the low-resource setting, however, they were not restricted to work on any particular aspect of the speech recognizer. We received 109 submissions from 18 research groups and evaluated the systems in terms of Word Error Rate on a blind test set. In this paper we summarize the data, approaches and results of the challenge.

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Section: Introductionmentioning

confidence: 99%

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language Using Cat Boost

Gupta

Singh²,

Singh

2022

Wireless Pers Commun

View full text Add to dashboard Cite

The pandemic caused due to COVID-19, has seen things going online. People tired of typing prefer to give voice commands. Most of the voice based applications and devices are not prepared to handle the native languages. Moreover, in a party environment it is difficult to identify a voice command as there are many speakers. The proposed work addresses the Cocktail party problem of Indian language, Gujarati. The voice response systems like, Siri, Alexa, Google Assistant as of now work on single voice command. The proposed algorithm G-Cocktail would help these applications to identify command given in Gujarati even from a mixed voice signal. Benchmark Dataset is taken from Microsoft and Linguistic Data Consortium for Indian Languages(LDC-IL) comprising single words and phrases. G-Cocktail utilizes the power of CatBoost algorithm to classify and identify the voice. Voice print of the entire sound files is created using Pitch, and Mel Frequency Cepstral Coefficients (MFCC). Seventy percent of the voice prints are used to train the network and thirty percent for testing. The proposed work is tested and compared with K-means, Naïve Bayes, and LightGBM.

show abstract

DA-IICT/IIITV System for Low Resource Speech Recognition Challenge 2018

Cited by 10 publications

References 22 publications

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language using CatBoost

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language using CatBoost

Interspeech 2018 Low Resource Automatic Speech Recognition Challenge for Indian Languages

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language Using Cat Boost

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