Description:

Inclusive speech technology: Developing automatic speech
recognition for everyone

Automatic speech recognition (ASR) is increasingly used, e.g., in emergency
response centres, domestic voice assistants, and search engines. Because of
the paramount relevance spoken language plays in our lives, it is critical that
ASR systems are able to deal with the variability in the way people speak (e.g.,
due to speaker differences, demographics, different speaking styles, and
differently abled users). ASR systems promise to deliver objective
interpretation of human speech. Practice and recent evidence however
suggests that the state-of-the-art ASRs struggle with the large variation in
speech due to e.g., gender, age, speech impairment, race, and accents. The
overarching goal in our research is to develop inclusive speech technology,
i.e., speech technology that works for everyone, irrespective of their voice,
language, and the way they talk. In this talk, I will present systematic
experiments aimed at quantifying, identifying the origin of, and mitigating the
bias of state-of-the-art ASRs on speech from different, typically low-resource,
groups of speakers, with a focus on bias against gender, age, regional accents
and non-native accents.