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 …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

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 …

Speaker adaptation techniques provide a powerful solution to customise automatic speech
recognition (ASR) systems for individual users. Practical application of unsupervised model …

We performed an experimental review of current diarization systems for the conversational
telephone speech (CTS) domain. In detail, we considered a total of eight different algorithms …

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 …

Conformer transducer achieves new state-of-the-art end-to-end (E2E) system performance
and has become increasingly appealing for production. In this paper, we study how to …

This paper presents an approach to long-context end-to-end automatic speech recognition
(ASR) using Transformers, aiming at improving ASR accuracy for long audio recordings …

Streaming speech recognition aims to transcribe speech to text in a streaming manner,
providing real-time speech interaction for smartphone users. However, it is not trivial to …