Humans can listen to a target speaker even in challenging acoustic conditions that have
noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail …

The processing of speech corrupted by interfering overlapping speakers is one of the
challenging problems with regards to today's automatic speech recognition systems …

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 …

This is a report of our lessons learned building acoustic models from 1 Million hours of
unlabeled speech, while labeled speech is restricted to 7,000 hours. We employ …

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

Automatic speech recognition (ASR) is helpful to improve quality of life. However, the
performance of ASR degrades in the case of noisy environment, limited privacy, and speech …

For real-world speech recognition applications, noise robustness is still a challenge. In this
work, we adopt the teacher-student (T/S) learning technique using a parallel clean and noisy …

In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components
in a far-field speaker system. Specifically, we use teacher-student (T/S) learning to adapt a …

Conventional far-field automatic speech recognition (ASR) systems typically employ
microphone array techniques for speech enhancement in order to improve robustness …

End-to-end (E2E) automatic speech recognition (ASR) models have been found to perform
well on general transcription tasks but often fail to correctly recognize words that occur …