3 accepted papers
Recent advances in unsupervised representation learning have demonstrated the impact of pretraining on large amounts of read speech. We adapt these techniques for domain adaptation in low-resource—both in terms of data and compute—conversational and broadcast domains. Moving beyond CTC, we pretrain…
Automatic speech recognition (ASR) systems are highly sensitive to train-test domain mismatch. However, because transcription is often prohibitively expensive, it is important to be able to make use of available transcribed out-of-domain data. We address the problem of domain adaptation with semi-su…
We present an importance sampling based approach to the active learning problem of selecting additional training data to supplement a seed model. Our proposed Δ-AUC selection optimizes AUC improvement in keyword search and is evaluated on the Spanish Fisher corpus. We show that over different traini…