Domain Specific Intent Classification of Sinhala Speech Data

Buddhika, Darshana; Liyadipita, Ranula; Nadeeshan, Sudeepa; Witharana, Hasini; Javasena, Sanath; Thayasivam, Uthayasanker

doi:10.1109/ialp.2018.8629103

Cited by 12 publications

(13 citation statements)

References 10 publications

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“…We used two different free-form speech command datasets to measure the accuracy of the proposed methodology. The first one is a Sinhala dataset and contains audio clips in the banking domain (Buddhika et al, 2018). Since it was difficult to find such other datasets for low-resource languages, we created another dataset in the Tamil language, Original Sinhala dataset contained 10 hours of speech data from 152 males and 63 females students in the age between 20 to 25 years.…”

Section: Datasetsmentioning

confidence: 99%

“…However, free-form commands are difficult to manage in this way since there can be overlappings between commands. Buddhika et al (2018); show some direct speech classification approaches to its intents. In particular, Buddhika et al (2018) have given some attention for the low resource setting.…”

Section: Introductionmentioning

confidence: 99%

“…Buddhika et al (2018); show some direct speech classification approaches to its intents. In particular, Buddhika et al (2018) have given some attention for the low resource setting. Additionally, Transfer learning is used to exploit the issue of limited data in some of the ASR based research (Huang et al, 2013;Kunze et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Here, we use a character probability map from an ASR model trained on English to identify intents. Performance of this methodology is evaluated using Sinhala (Buddhika et al, 2018) and newly collected Tamil datasets. The proposed approach can reach to a reasonable accuracy using limited training data.…”

Section: Introductionmentioning

confidence: 99%

“…;;Buddhika et al (2018) has benefited from direct speech classification approach. Additionally, as shown in the work ofLee et al (2015); Liu et al…”

mentioning

confidence: 99%

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Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages

Karunanayake

Thayasivam

Ranathunga

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Current state-of-the-art speech-based user interfaces use data intense methodologies to recognize free-form speech commands. However, this is not viable for low-resource languages, which lack speech data. This restricts the usability of such interfaces to a limited number of languages. In this paper, we propose a methodology to develop a robust domainspecific speech command classification system for low-resource languages using speech data of a high-resource language. In this transfer learning-based approach, we used a Convolution Neural Network (CNN) to identify a fixed set of intents using an ASR-based character probability map. We were able to achieve significant results for Sinhala and Tamil datasets using an English based ASR, which attests the robustness of the proposed approach. .

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…;;Buddhika et al (2018) has benefited from direct speech classification approach. Additionally, as shown in the work ofLee et al (2015); Liu et al…”

mentioning

confidence: 99%

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