Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art
results in end-to-end automatic speech recognition (ASR). Recently, the transformer …

Artificial Neural Networks (ANNs) were created inspired by the neural networks in the
human brain and have been widely applied in speech processing. The application areas of …

Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled
speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine …

Non-autoregressive transformer models have achieved extremely fast inference speed and
comparable performance with autoregressive sequence-to-sequence models in neural …

The RNN transducer is a promising end-to-end model candidate. We compare the original
training criterion with the full marginalization over all alignments, to the commonly used …

Self-supervised pretraining for Automated Speech Recognition (ASR) has shown varied
degrees of success. In this paper, we propose to jointly learn representations during …

This paper addresses end-to-end automatic speech recognition (ASR) for long audio
recordings such as lecture and conversational speeches. Most end-to-end ASR models are …

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 …

Semi-supervised learning has demonstrated promising results in automatic speech
recognition (ASR) by self-training using a seed ASR model with pseudo-labels generated for …