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        <title>Utilizing POS-Driven Pitch Contour Analysis for Enhanced Tamil Text-to-Speech Synthesis</title>
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    <name type="personal">
        <namePart type="given">Preethi</namePart>
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        <namePart type="family">Vijayalakshmi</namePart>
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        <namePart type="family">Nagarajan</namePart>
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        <namePart type="given">Malarvizhi</namePart>
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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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            <namePart type="given">Karunesh</namePart>
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    <abstract>A novel approach to text-to-speech synthesis that integrates pitch contour labels derived from the highest occurrence analysis for each Part-of-Speech (POS) tag. Using the Stanford POS Tagger, grammatical tags are assigned to words, and the most frequently occurring pitch contour labels associated with these tags are analyzed, focusing on both unigram and bigram statistics. The primary goal is to identify the pitch contour for each POS tag based on its frequency of occurrence. These pitch contour labels are incorporated into the output of the synthesized waveform using the TD-PSOLA (Time Domain Pitch Synchronous Overlap and Add) signal processing algorithm. The resulting waveform is evaluated using Mean Opinion Scores (MOS), demonstrating significant enhancements in quality and producing a prosodically rich synthetic speech.</abstract>
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            <start>269</start>
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%0 Conference Proceedings
%T Utilizing POS-Driven Pitch Contour Analysis for Enhanced Tamil Text-to-Speech Synthesis
%A Thinakaran, Preethi
%A Gladston, Anushiya Rachel
%A Vijayalakshmi, P.
%A Nagarajan, T.
%A Muthuramalingam, Malarvizhi
%A S, Sooriya
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F thinakaran-etal-2024-utilizing
%X A novel approach to text-to-speech synthesis that integrates pitch contour labels derived from the highest occurrence analysis for each Part-of-Speech (POS) tag. Using the Stanford POS Tagger, grammatical tags are assigned to words, and the most frequently occurring pitch contour labels associated with these tags are analyzed, focusing on both unigram and bigram statistics. The primary goal is to identify the pitch contour for each POS tag based on its frequency of occurrence. These pitch contour labels are incorporated into the output of the synthesized waveform using the TD-PSOLA (Time Domain Pitch Synchronous Overlap and Add) signal processing algorithm. The resulting waveform is evaluated using Mean Opinion Scores (MOS), demonstrating significant enhancements in quality and producing a prosodically rich synthetic speech.
%U https://aclanthology.org/2024.icon-1.32/
%P 269-273

Markdown (Informal)

[Utilizing POS-Driven Pitch Contour Analysis for Enhanced Tamil Text-to-Speech Synthesis](https://aclanthology.org/2024.icon-1.32/) (Thinakaran et al., ICON 2024)

• Utilizing POS-Driven Pitch Contour Analysis for Enhanced Tamil Text-to-Speech Synthesis (Thinakaran et al., ICON 2024)

ACL

• Preethi Thinakaran, Anushiya Rachel Gladston, P Vijayalakshmi, T Nagarajan, Malarvizhi Muthuramalingam, and Sooriya S. 2024. Utilizing POS-Driven Pitch Contour Analysis for Enhanced Tamil Text-to-Speech Synthesis. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 269–273, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).