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 …

This paper proposes a token-level serialized output training (t-SOT), a novel framework for
streaming multi-talker automatic speech recognition (ASR). Unlike existing streaming multi …

We propose a general framework to compute the word error rate (WER) of ASR systems that
process recordings containing multiple speakers at their input and that produce multiple …

Streaming end-to-end multi-talker speech recognition aims at transcribing the overlapped
speech from conversations or meetings with an all-neural model in a streaming fashion …

This paper presents a novel framework for joint speaker diarization (SD) and automatic
speech recognition (ASR), named SLIDAR (sliding-window diarization-augmented …

The Streaming Unmixing and Recognition Transducer (SURT) has recently become a
popular framework for continuous, streaming, multi-talker speech recognition (ASR). With …

Extending the RNN Transducer (RNNT) to recognize multi-talker speech is essential for
wider automatic speech recognition (ASR) applications. Multi-talker RNNT (MT-RNNT) aims …

Streaming recognition and segmentation of multi-party conversations with overlapping
speech is crucial for the next generation of voice assistant applications. In this work we …

In recent years, there have been studies to further improve the end-to-end neural speaker
diarization (EEND) systems. This letter proposes the EEND-DEMUX model, a novel …

Many of the recent advances in speech separation are primarily aimed at synthetic mixtures
of short audio utterances with high degrees of overlap. Most of these approaches need an …