Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

The performance of automatic speech recognition (ASR) systems typically degrades
significantly when the training and test data domains are mismatched. In this paper, we …

End-to-end automatic speech recognition (ASR) has become a popular alternative to
traditional module-based systems, simplifying the model-building process with a single deep …

Robustness against noise is critical for modulation recognition (MR) approaches deployed
in real-world communication systems. In MR systems, a corrupted signal is normally …

Pseudo-labeling (PL) has been shown to be effective in semi-supervised automatic speech
recognition (ASR), where a base model is self-trained with pseudo-labels generated from …

When labeled data is insufficient, pseudo-labeling based semi-supervised learning can
significantly improve the performance of automatic speech recognition. However, pseudo …

This paper presents novel Weighted Finite-State Transducer (WFST) topologies to
implement Connectionist Temporal Classification (CTC)-like algorithms for automatic …

Radio frequency (RF) fingerprinting is a promising device authentication technique for
securing the Internet of Things. It exploits the intrinsic and unique hardware impairments of …

In this paper, we propose a self-training approach for automatic speech recognition (ASR)
for low-resource settings. While self-training approaches have been extensively developed …

Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a target sequence.
The Connectionist Temporal Classification (CTC) criterion is widely used in multiple …