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 presents a novel streaming automatic speech recognition (ASR) framework for
multi-talker overlapping speech captured by a distant microphone array with an arbitrary …

This paper proposes serialized output training (SOT), a novel framework for multi-speaker
overlapped speech recognition based on an attention-based encoder-decoder approach …

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 present a frontend for improving robustness of automatic speech recognition (ASR), that
jointly implements three modules within a single model: acoustic echo cancellation, speech …

Transcribing meetings containing overlapped speech with only a single distant microphone
(SDM) has been one of the most challenging problems for automatic speech recognition …

End-to-end multi-talker speech recognition is an emerging research trend in the speech
community due to its vast potential in applications such as conversation and meeting …

Recent research shows end-to-end ASR systems can recognize overlapped speech from
multiple speakers. However, all published works have assumed no latency constraints …

Automatic speech recognition (ASR) of single channel far-field recordings with an unknown
number of speakers is traditionally tackled by cascaded modules. Recent research shows …

Although automatic speech recognition (ASR) can perform well in common non-overlapping
environments, sustaining performance in multi-talker overlapping speech recognition …