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 …

Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …

In this paper, we summarize recent progresses made in deep learning based acoustic
models and the motivation and insights behind the surveyed techniques. We first discuss …

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 …

Recently, fully recurrent neural network (RNN) based end-to-end models have been proven
to be effective for multi-speaker speech recognition in both the single-channel and multi …

We propose an end-to-end speaker-attributed automatic speech recognition model that
unifies speaker counting, speech recognition, and speaker identification on monaural …

Speaker-aware source separation methods are promising workarounds for major difficulties
such as arbitrary source permutation and unknown number of sources. However, it remains …

Lyrics are the words that make up a song, while chords are harmonic sets of multiple notes
in music. Lyrics and chords are generally essential information in music, ie unaccompanied …

The cocktail party problem, ie, tracing and recognizing the speech of a specific speaker
when multiple speakers talk simultaneously, is one of the critical problems yet to be solved …