Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
| Autor: | Bajibabu Bollepalli, Dhananjaya Gowda, Sudarsana Reddy Kadiri, Paavo Alku |
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| Přispěvatelé: | Dept Signal Process and Acoust, Speech Communication Technology, Aalto-yliopisto, Aalto University |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Sound (cs.SD) linear prediction General Computer Science Computer science Linear prediction Tracking (particle physics) Computer Science - Sound Machine Learning (cs.LG) Reduction (complexity) Audio and Speech Processing (eess.AS) formant tracking FOS: Electrical engineering electronic engineering information engineering General Materials Science dynamic programming Signal processing Artificial neural network business.industry Deep learning Autocorrelation deep neural network General Engineering deep neural net TK1-9971 Formant Speech analysis Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business Algorithm Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | IEEE Access, Vol 9, Pp 151631-151640 (2021) |
| DOI: | 10.48550/arxiv.2201.01525 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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