Multi-talker speech recognition and target-talker speech recognition, both involve
transcription in multi-talker contexts, remain significant challenges. However, existing …

Benefiting from massive and diverse data sources, speech foundation models exhibit strong
generalization and knowledge transfer capabilities to a wide range of downstream tasks …

Recent advancements in large language models (LLMs) have revolutionized various
domains, bringing significant progress and new opportunities. Despite progress in speech …

We propose a novel approach to enable the use of large, single speaker ASR models, such
as Whisper, for target speaker ASR. The key insight of this method is that it is much easier to …

This letter presents a new target speech recognition problem, where the target speech is
defined by a keyword. For instance, when a person speaks “Hey Google” or “Help Me”, we …

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 …

Target-speaker automatic speech recognition (ASR) aims to transcribe the desired speech
of a target speaker from multi-talker overlapped utterances. Most of the existing target …

Target-speaker speech processing (TS) tasks, such as target-speaker automatic speech
recognition (TS-ASR), target speech extraction (TSE), and personal voice activity detection …

In many real-world scenarios, such as meetings, multiple speakers are present with an
unknown number of participants, and their utterances often overlap. We address these multi …

In recent years, Transformer networks have shown remarkable performance in speech
recognition tasks. However, their deployment poses challenges due to high computational …