Towards robust heart failure detection in digital telephony environments by utilizing transformer-based codec inversion

This study introduces the Codec Transformer Network (CTN) to enhance the reliability of automatic heart failure (HF) detection from coded telephone speech by addressing codec-related challenges in digital telephony. The study specifically addresses the codec mismatch between training and inference in HF detection. CTN is designed to map the mel-spectrogram representations of encoded speech signals back to their original, non-encoded forms, thereby recovering HF-related discriminative information. The effectiveness of CTN is demonstrated in conjunction with three HF detectors, based on Support Vector Machine, Random Forest, and K-Nearest Neighbors classifiers. The results show that CTN effectively retrieves the discriminative information between patients and controls, and performs comparably to or better than a baseline approach, based on multi-condition training. • The codec inversion method enhances heart failure detection from telephone speech. • More robust heart failure detection in environments with varying codecs. • Codec mismatch is reduced by utilizing transformer-based pre-trained models.