Fine-tuned MMS outperforms XLS-R on fieldwork ASR with less than one hour of training data, while XLS-R reaches parity beyond one hour.
The Zero Resource Speech Challenge 2019: TTS without T
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abstract
We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text). We provide raw audio for a target voice in an unknown language (the Voice dataset), but no alignment, text or labels. Participants must discover subword units in an unsupervised way (using the Unit Discovery dataset) and align them to the voice recordings in a way that works best for the purpose of synthesizing novel utterances from novel speakers, similar to the target speaker's voice. We describe the metrics used for evaluation, a baseline system consisting of unsupervised subword unit discovery plus a standard TTS system, and a topline TTS using gold phoneme transcriptions. We present an overview of the 19 submitted systems from 10 teams and discuss the main results.
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Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages
Fine-tuned MMS outperforms XLS-R on fieldwork ASR with less than one hour of training data, while XLS-R reaches parity beyond one hour.