Single-channel, speaker-independent speech separation methods have recently seen great
progress. However, the accuracy, latency, and computational cost of such methods remain …

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 …

This paper addresses the problem of single channel speech recognition of a target speaker
in a mixture of speech signals. We propose to exploit auxiliary speaker information provided …

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

Recently, end-to-end models have become a popular approach as an alternative to
traditional hybrid models in automatic speech recognition (ASR). The multi-speaker speech …

Recently, the end-to-end approach has proven its efficacy in monaural multi-speaker speech
recognition. However, high word error rates (WERs) still prevent these systems from being …

Far-field multi-speaker automatic speech recognition (ASR) has drawn increasing attention
in recent years. Most existing methods feature a signal processing frontend and an ASR …

Unsupervised single-channel overlapped speech recognition is one of the hardest problems
in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the …

The combination of a deep neural network (DNN)-based speech enhancement (SE) front-
end and an automatic speech recognition (ASR) back-end is a widely used approach to …

Most approaches to multi-talker overlapped speech separation and recognition assume that
the number of simultaneously active speakers is given, but in realistic situations, it is typically …