Recent advancements in deep learning (DL) have posed a significant challenge for
automatic speech recognition (ASR). ASR relies on extensive training datasets, including …

With the emergence of deep learning, the performance of automatic speech recognition
(ASR) systems has remarkably improved. Especially for resource-rich languages such as …

Communication has been an important aspect of human life, civilization, and globalization
for thousands of years. Biometric analysis, education, security, healthcare, and smart cities …

The rapid growth of artificial intelligence (AI) has given rise to the field of Human-AI
Interaction (HAII). This study meticulously reviewed the research themes, theoretical …

Significant research has been conducted during the last decade on the application of
machine learning for speech processing, particularly speech recognition. However, in recent …

Code-switching (CS) in spoken language is where the speech has two or more languages
within an utterance. It is an unsolved issue in automatic speech recognition (ASR) research …

Unsupervised speech recognition (unsupervised ASR) aims to learn the ASR system with
non-parallel speech and text corpus only. Wav2vec-U [1] has shown promising results in …

Microsystems with capabilities of acoustic signal perception and recognition are widely used
in unattended monitoring applications. In order to realize long-term and large-scale …

The research problem addressed in this study is how to effectively combine multimodal data
from imperfect text transcripts and raw audio in a deep framework for automatic speech …

The electrocardiogram (ECG) signal is made up of sequences of three distinct waves,
including the P-wave, QRS-complex, and T-wave. These sequences may contain several …