Categorization of Speech Frequency Following Responses to English Vowels
- Resource Type
- Conference
- Authors
- Zuo, Ruichen; Zhang, Xingzhong; Zhao, Jiying
- Source
- 2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Electrical and Computer Engineering (CCECE), 2023 IEEE Canadian Conference on. :99-104 Sep, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Network topology
Machine learning
Brain modeling
Hearing aids
Feature extraction
Robustness
Frequency Following Response
Convolutional Neural Network
Motif topology
- Language
- ISSN
- 2576-7046
This paper investigates the categorization of the speech Frequency Following Response (sFFR) evoked by the five English vowels using machine learning models. As part of the control system of the brain-controlled hearing aid, the models based on Convolution Neural Networks (CNNs) are used to extract spectrum features from sFFR signals to classify vowels. The highest average accuracy reaches 80.00% for CNN with loose recurrent connections. In addition, we tested the performance of models when there is different additive noise to the input sFFR signals and found that the loose recurrent connections help the CNN model to obtain stronger robustness. We also use motif topology to analyze the content features of the lateral connection network after training. At last, we compared our work with other research. The results of this work show the potential of the proposed network models for the categorization of sFFR signals applied to brain-controlled hearing aids, especially in the presence of noise.