End-to-end automatic speech recognition (ASR) has become an increasingly popular area
of research, with two main models being online and offline ASR. Online models aim to …

This work studies the use of attention masking in transformer transducer based speech
recognition for building a single configurable model for different deployment scenarios. We …

Long utterances segmentation is crucial in end-to-end (E2E) streaming automatic speech
recognition (ASR). However, commonly used voice activity detection (VAD)-based and fixed …

We explore unifying a neural segmenter with two-pass cascaded encoder ASR into a single
model. A key challenge is allowing the segmenter (which runs in real-time, synchronously …

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 …

Incremental speech recognizers start displaying results while the users are still speaking.
These partial results are beneficial to users who like the responsiveness of the system …

End-to-end automatic speech recognition (E2E-ASR) can be classified into several network
architectures, such as connectionist temporal classification (CTC), recurrent neural network …

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 …

End-to-end (E2E) automatic speech recognition (ASR) can operate in two modes: streaming
and non-streaming, each with its pros and cons. Streaming ASR processes the speech …

During conversations, humans are capable of inferring the intention of the speaker at any
point of the speech to prepare the following action promptly. Such ability is also the key for …