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 [BibTeX] [Marc21]
On The Relationship Between Speech-based Breathing Signal Prediction Evaluation Measures And Breathing Parameters Estimation
Type of publication: Conference paper
Citation: Mostaani_ICASSP_2021
Publication status: Published
Booktitle: Proc. of ICASSP
Year: 2021
Abstract: The respiratory system is one of the major components of the speech production system. Any alteration in breathing can result in changes in speech. Specific breathing characteristics, such as breathing rate and tidal volume, can indicate a person's pathological condition. More recently, neural network-based methods have started emerging for predicting the breathing signal from the speech signal. The neural networks are trained and evaluated with different objective measures, such as mean squared error (MSE) and Pearson's correlation. This paper investigates whether there is a systematic relationship between the different objective measures used for training and evaluating the neural network models and the end-goal, i.e. estimation of breathing parameters such as, breathing rate and tidal volume. Our investigations on two different data sets with two different neural network-based approaches show that there is no clear systematic relationship. In other words, obtaining a high Pearson's correlation on the evaluation set does not necessarily mean better breathing parameter estimation. Thus, indicating the need for developing other objective evaluation measures.
Projects TIPS
Authors Mostaani, Zohreh
Nallanthighal, Venkata Srikanth
Härmä, Aki
Strik, Helmer
Magimai.-Doss, Mathew
Added by: [UNK]
Total mark: 0
  • Mostaani_ICASSP_2021.pdf