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        <title>Spectral modification for recognition of children’s speech undermismatched conditions</title>
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    <name type="personal">
        <namePart type="given">Hemant</namePart>
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        <namePart type="given">Sudarsana</namePart>
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    <abstract>In this paper, we propose spectral modification by sharpening formants and by reducing the spectral tilt to recognize children’s speech by automatic speech recognition (ASR) systems developed using adult speech. In this type of mismatched condition, the ASR performance is degraded due to the acoustic and linguistic mismatch in the attributes between children and adult speakers. The proposed method is used to improve the speech intelligibility to enhance the children’s speech recognition using an acoustic model trained on adult speech. In the experiments, WSJCAM0 and PFSTAR are used as databases for adults’ and children’s speech, respectively. The proposed technique gives a significant improvement in the context of the DNN-HMM-based ASR. Furthermore, we validate the robustness of the technique by showing that it performs well also in mismatched noise conditions.</abstract>
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        <url>https://aclanthology.org/2021.nodalida-main.10</url>
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        <date>2021-may 31–2 jun</date>
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%0 Conference Proceedings
%T Spectral modification for recognition of children’s speech undermismatched conditions
%A Kathania, Hemant Kumar
%A Kadiri, Sudarsana Reddy
%A Alku, Paavo
%A Kurimo, Mikko
%Y Dobnik, Simon
%Y Øvrelid, Lilja
%S Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2021
%8 may 31–2 jun
%I Linköping University Electronic Press, Sweden
%C Reykjavik, Iceland (Online)
%F kathania-etal-2021-spectral
%X In this paper, we propose spectral modification by sharpening formants and by reducing the spectral tilt to recognize children’s speech by automatic speech recognition (ASR) systems developed using adult speech. In this type of mismatched condition, the ASR performance is degraded due to the acoustic and linguistic mismatch in the attributes between children and adult speakers. The proposed method is used to improve the speech intelligibility to enhance the children’s speech recognition using an acoustic model trained on adult speech. In the experiments, WSJCAM0 and PFSTAR are used as databases for adults’ and children’s speech, respectively. The proposed technique gives a significant improvement in the context of the DNN-HMM-based ASR. Furthermore, we validate the robustness of the technique by showing that it performs well also in mismatched noise conditions.
%U https://aclanthology.org/2021.nodalida-main.10
%P 94-100

Markdown (Informal)

[Spectral modification for recognition of children’s speech undermismatched conditions](https://aclanthology.org/2021.nodalida-main.10) (Kathania et al., NoDaLiDa 2021)

• Spectral modification for recognition of children’s speech undermismatched conditions (Kathania et al., NoDaLiDa 2021)

ACL

• Hemant Kumar Kathania, Sudarsana Reddy Kadiri, Paavo Alku, and Mikko Kurimo. 2021. Spectral modification for recognition of children’s speech undermismatched conditions. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), pages 94–100, Reykjavik, Iceland (Online). Linköping University Electronic Press, Sweden.