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 …

Large Language models (LLM) have demonstrated the capability to handle a variety of
generative tasks. This paper presents the UniAudio system, which, unlike prior task-specific …

Recent advancements in generative speech models based on audio-text prompts have
enabled remarkable innovations like high-quality zero-shot text-to-speech. However …

Speaker individuality information is among the most critical elements within speech signals.
By thoroughly and accurately modeling this information, it can be utilized in various …

Audio generation is a major branch of generative AI research. Compared with prior works in
this area that are commonly task-specific with heavy domain knowledge, this paper …

Speaker extraction aims to extract the target speech signal from a multi-talker environment
given a target speaker's reference speech. We recently proposed a time-domain solution …

This study proposes a cross-domain multi-objective speech assessment model, called
MOSA-Net, which can simultaneously estimate the speech quality, intelligibility, and …

This paper investigates a self-adaptation method for speech enhancement using auxiliary
speaker-aware features; we extract a speaker representation used for adaptation directly …

The machine recognition of speech spoken at a distance from the microphones, known as
far-field automatic speech recognition (ASR), has received a significant increase in attention …

In crowded settings, the human brain can focus on speech from a target speaker, given prior
knowledge of how they sound. We introduce a novel intelligent hearable system that …