23.6k citations
- Carnegie Mellon UniversityUS49 papers
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- University of Illinois Urbana-ChampaignUS22 papers
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Showing 2018 · eess.ASShow all
3 papers · 2 filters
eess.AS2018★ 8 cited
Towards achieving robust universal neural vocoding
Jaime Lorenzo-Trueba, Thomas Drugman, Javier Latorre +5
This paper explores the potential universality of neural vocoders. We train a WaveRNN-based vocoder on 74 speakers coming from 17 languages. This vocoder is shown to be capable of…
eess.AS2018★ 1 cited
When CTC Training Meets Acoustic Landmarks
Di He, Xuesong Yang, Boon Pang Lim +3
Connectionist temporal classification (CTC) provides an end-to-end acoustic model (AM) training strategy. CTC learns accurate AMs without time-aligned phonetic transcription, but s…
eess.AS2018★ 4 cited
Learning Noise-Invariant Representations for Robust Speech Recognition
Davis Liang, Zhiheng Huang, Zachary C. Lipton
Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs. Small amounts of noise can destroy the performance of an o…