Spectral modification for recognition of children’s speech under mismatched conditions

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n 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.
Original languageEnglish
Title of host publicationProceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)
Place of PublicationSweden
PublisherLinköping University Electronic Press
Number of pages7
ISBN (Electronic)978-91-7929-614-8
Publication statusPublished - 31 May 2021
MoE publication typeA4 Conference publication
EventNordic Conference on Computational Linguistics - Reykjavik, Iceland
Duration: 31 May 20212 Jun 2021

Publication series

NameLinköping electronic conference proceedings
ISSN (Print)1650-3686
ISSN (Electronic)1650-3740


ConferenceNordic Conference on Computational Linguistics
Abbreviated titleNoDaLiDa


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