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 …

For various speech-related tasks, confidence scores from a speech recogniser are a useful
measure to assess the quality of transcriptions. In traditional hidden Markov model-based …

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay
further progression. This paper presents the development of a state-of-the-art Conformer …

Recently, there has been increasing progress in end-to-end automatic speech recognition
(ASR) architecture, which transcribes speech to text without any pre-trained alignments. One …

Contextual knowledge is important for real-world automatic speech recognition (ASR)
applications. In this paper, a novel tree-constrained pointer generator (TCPGen) component …

The traditional hybrid deep neural network (DNN)–hidden Markov model (HMM) system and
attention-based encoder–decoder (AED) model are both commonly used automatic speech …

Contextual knowledge is essential for reducing speech recognition errors on high-valued
long-tail words. This paper proposes a novel tree-constrained pointer generator (TCPGen) …

In this work, we investigate two popular end-to-end automatic speech recognition (ASR)
models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN …

End-to-end models with auto-regressive decoders have shown impressive results for
automatic speech recognition (ASR). These models formulate the sequence-level probability …

This paper introduces a fast-slow encoder based transducer with streaming deliberation for
end-to-end automatic speech recognition. We aim to improve the recognition accuracy of the …