Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks

Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation methods were first compared. The six methods include linear prediction (LP) algorithms, weighted LP algorithms and the recentl...

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Autores principales:	Dhananjaya N. Gowda, Bajibabu Bollepalli, Sudarsana Reddy Kadiri, Paavo Alku
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Speech analysis formant tracking linear prediction dynamic programming deep neural net Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/d3e5f843aaf44751a9b53d75a6163f7e
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spelling	oai:doaj.org-article:d3e5f843aaf44751a9b53d75a6163f7e2021-11-17T00:00:38ZFormant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks2169-353610.1109/ACCESS.2021.3126280https://doaj.org/article/d3e5f843aaf44751a9b53d75a6163f7e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606741/https://doaj.org/toc/2169-3536Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation methods were first compared. The six methods include linear prediction (LP) algorithms, weighted LP algorithms and the recently developed quasi-closed phase forward-backward (QCP-FB) method. QCP-FB gave the best performance in the comparison. Therefore, a novel formant tracking approach, which combines benefits of deep learning and signal processing based on QCP-FB, was proposed. In this approach, the formants predicted by a DNN-based tracker from a speech frame are refined using the peaks of the all-pole spectrum computed by QCP-FB from the same frame. Results show that the proposed DNN-based tracker performed better both in detection rate and estimation error for the lowest three formants compared to reference formant trackers. Compared to the popular Wavesurfer, for example, the proposed tracker gave a reduction of 29%, 48%, and 35% in the estimation error for the lowest three formants, respectively.Dhananjaya N. GowdaBajibabu BollepalliSudarsana Reddy KadiriPaavo AlkuIEEEarticleSpeech analysisformant trackinglinear predictiondynamic programmingdeep neural netElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151631-151640 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Speech analysis formant tracking linear prediction dynamic programming deep neural net Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Speech analysis formant tracking linear prediction dynamic programming deep neural net Electrical engineering. Electronics. Nuclear engineering TK1-9971 Dhananjaya N. Gowda Bajibabu Bollepalli Sudarsana Reddy Kadiri Paavo Alku Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
description	Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation methods were first compared. The six methods include linear prediction (LP) algorithms, weighted LP algorithms and the recently developed quasi-closed phase forward-backward (QCP-FB) method. QCP-FB gave the best performance in the comparison. Therefore, a novel formant tracking approach, which combines benefits of deep learning and signal processing based on QCP-FB, was proposed. In this approach, the formants predicted by a DNN-based tracker from a speech frame are refined using the peaks of the all-pole spectrum computed by QCP-FB from the same frame. Results show that the proposed DNN-based tracker performed better both in detection rate and estimation error for the lowest three formants compared to reference formant trackers. Compared to the popular Wavesurfer, for example, the proposed tracker gave a reduction of 29%, 48%, and 35% in the estimation error for the lowest three formants, respectively.
format	article
author	Dhananjaya N. Gowda Bajibabu Bollepalli Sudarsana Reddy Kadiri Paavo Alku
author_facet	Dhananjaya N. Gowda Bajibabu Bollepalli Sudarsana Reddy Kadiri Paavo Alku
author_sort	Dhananjaya N. Gowda
title	Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
title_short	Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
title_full	Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
title_fullStr	Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
title_full_unstemmed	Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
title_sort	formant tracking using quasi-closed phase forward-backward linear prediction analysis and deep neural networks
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/d3e5f843aaf44751a9b53d75a6163f7e
work_keys_str_mv	AT dhananjayangowda formanttrackingusingquasiclosedphaseforwardbackwardlinearpredictionanalysisanddeepneuralnetworks AT bajibabubollepalli formanttrackingusingquasiclosedphaseforwardbackwardlinearpredictionanalysisanddeepneuralnetworks AT sudarsanareddykadiri formanttrackingusingquasiclosedphaseforwardbackwardlinearpredictionanalysisanddeepneuralnetworks AT paavoalku formanttrackingusingquasiclosedphaseforwardbackwardlinearpredictionanalysisanddeepneuralnetworks
_version_	1718426076760244224

Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks

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