The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

This survey paper provides a comprehensive overview of the recent advancements and
challenges in applying large language models to the field of audio signal processing. Audio …

We present BERT-CTC-Transducer (BECTRA), a novel end-to-end automatic speech
recognition (E2E-ASR) model formulated by the transducer with a BERT-enhanced encoder …

RNN Tranducer (RNN-T) technology is very popular for building deployable models for end-
to-end (E2E) automatic speech recognition (ASR) and spoken language understanding …

Non-autoregressive automatic speech recognition (NASR) models have gained attention
due to their parallelism and fast inference. The encoder-based NASR, eg connectionist …

Transferring the knowledge of large language models (LLMs) is a promising technique to
incorporate linguistic knowledge into end-to-end automatic speech recognition (ASR) …

Dialog history enhances downstream classification performance in both speech and text
based dialog systems. However, there still exists a gap in dialog history integration in a fully …

Much of the recent progress in automatic speech recognition (ASR) lies in developing an
acoustic encoder, such as enlarging its capacity and designing a refined architecture for …

Although end-to-end (E2E) automatic speech recognition (ASR) has shown state-of-the-art
recognition accuracy, it tends to be implicitly biased towards the training data distribution …

Attention-based encoder-decoder models with autoregressive (AR) decoding have proven
to be the dominant approach for automatic speech recognition (ASR) due to their superior …