Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing

Cámbara Ruiz, Guillermo; Luque, Jordi; Farrús, Mireia

doi:http://dx.doi.org/10.1109/ICASSP40776.2020.9052972

Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing

Citation

Cámbara G, Luque J, Farrús M. Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing. In: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Proceedings; 2020 May 4-8; Barcelona, Spain. New York: IEEE; 2020. p. 7564-8. DOI: 10.1109/ICASSP40776.2020.9052972

Abstract

The use of photoplethysmogram signal (PPG) for heart and sleep monitoring is commonly found nowadays in smart-phones and wrist wearables. Besides common usages, it has been proposed and reported that person information can be extracted from PPG for other uses, like biometry tasks. In this work, we explore several end-to-end convolutional neural network architectures for detection of human’s characteristics such as gender or person identity. In addition, we evaluate whether speech/non-speech events may be inferred from PPG signal, where speech might translate in fluctuations into the pulse signal. The obtained results are promising and clearly show the potential of fully end-to-end topologies for automatic extraction of meaningful biomarkers, even from a noisy signal sampled by a low-cost PPG sensor. The AUCs for best architectures put forward PPG wave as biological discriminant, reaching 79% and 89.0%, respectively for gender and person verification tasks. Furthermore, speech detection experiments reporting AUCs around 69% encourage us for further exploration about the feasibility of PPG for speech processing tasks.

Description

Comunicació presentada a: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), celebrat de manera virtual a Barcelona del 4 al 8 de maig de 2020.