Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning

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Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (2019, neutral): “While general form of acoustic variabilities have been studied [27], there is some work specifically on speaker invariance [28, 29].”
Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling (2019, result): “The amount of improvement is in accordance with the findings reported in [17], despite that [17] exploited English transcriptions during training.”
To Reverse the Gradient or Not: an Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognition (2018, methodology): “This approach has been previously used for speech recognition to learn invariance to noise conditions [9], speaker identity [10, 11, 12] and accent [13].”
Unsupervised Adaptation of Acoustic Models for ASR Using Utterance-Level Embeddings from Squeeze and Excitation Networks (2019, methodology): “Such networks have been used for achieving speaker invariance [24, 25, 26], noise invariance [27, 28], etc.”
Virtual Phone Discovery for Speech Synthesis Without Text (2019, methodology): “Multi-task learning bottleneck features which were speaker invariant were proposed for the ABX task [12].”