Meta's newest dataset will train speech recognition engines on 'clusters' of speakers
. However, many of the datasets used to train ASR models are organized by demographic — age group, gender, nationality, English accent — which limit the variation of pronunciations that models are trained on, ultimately hindering their function in understanding a broad cross section of users.
To get around this, Meta AI has developed a dataset that instead relies on an utterance clustering method. “Instead of dividing a dataset based on speakers’ demographic information … our proposed algorithm clusters speech at the utterance level,” the Meta AI team explained in Wednesday’s blog post. “A single cluster will contain similar utterances from a diverse group of speakers.
Meta’s resulting dataset includes just over 27,000 command utterances collected from 595 paid US volunteers. Their utterances revolve around seven main themes — music, capture, utilities, notification control, messaging, calling and dictation — that other researchers can then use to train their own models and digital assistants on. Prompts included asking the speakers how they’d voice search for a song or make plans with friends and deciding where to meet up.
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