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        <title>Synthesizing Audio for Hindi WordNet</title>
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        <namePart type="given">Diptesh</namePart>
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    <abstract>In this paper, we describe our work on the creation of a voice model using a speech synthesis system for the Hindi Language. We use pre-existing “voices”, use publicly available speech corpora to create a “voice” using the Festival Speech Synthesis System (Black, 1997). Our contribution is two-fold: (1) We scrutinize multiple speech synthesis systems and provide an extensive report on the currently available state-of-the-art systems. We also develop voices using the existing implementations of the aforementioned systems, and (2) We use these voices to generate sample audios for randomly chosen words; manually evaluate the audio generated, and produce audio for all WordNet words using the winner voice model. We also produce audios for the Hindi WordNet Glosses and Example sentences. We describe our efforts to use pre-existing implementations for WaveNet - a model to generate raw audio using neural nets (Oord et al., 2016) and generate speech for Hindi. Our lexicographers perform a manual evaluation of the audio generated using multiple voices. A qualitative and quantitative analysis reveals that the voice model generated by us performs the best with an accuracy of 0.44.</abstract>
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%0 Conference Proceedings
%T Synthesizing Audio for Hindi WordNet
%A Kanojia, Diptesh
%A Jyothi, Preethi
%A Bhattacharyya, Pushpak
%Y Bond, Francis
%Y Vossen, Piek
%Y Fellbaum, Christiane
%S Proceedings of the 9th Global Wordnet Conference
%D 2018
%8 January
%I Global Wordnet Association
%C Nanyang Technological University (NTU), Singapore
%F kanojia-etal-2018-synthesizing
%X In this paper, we describe our work on the creation of a voice model using a speech synthesis system for the Hindi Language. We use pre-existing “voices”, use publicly available speech corpora to create a “voice” using the Festival Speech Synthesis System (Black, 1997). Our contribution is two-fold: (1) We scrutinize multiple speech synthesis systems and provide an extensive report on the currently available state-of-the-art systems. We also develop voices using the existing implementations of the aforementioned systems, and (2) We use these voices to generate sample audios for randomly chosen words; manually evaluate the audio generated, and produce audio for all WordNet words using the winner voice model. We also produce audios for the Hindi WordNet Glosses and Example sentences. We describe our efforts to use pre-existing implementations for WaveNet - a model to generate raw audio using neural nets (Oord et al., 2016) and generate speech for Hindi. Our lexicographers perform a manual evaluation of the audio generated using multiple voices. A qualitative and quantitative analysis reveals that the voice model generated by us performs the best with an accuracy of 0.44.
%U https://aclanthology.org/2018.gwc-1.49
%P 388-393

Markdown (Informal)

[Synthesizing Audio for Hindi WordNet](https://aclanthology.org/2018.gwc-1.49) (Kanojia et al., GWC 2018)

• Synthesizing Audio for Hindi WordNet (Kanojia et al., GWC 2018)

ACL

• Diptesh Kanojia, Preethi Jyothi, and Pushpak Bhattacharyya. 2018. Synthesizing Audio for Hindi WordNet. In Proceedings of the 9th Global Wordnet Conference, pages 388–393, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.