Watermarking for TTS Paper Review
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π Paper: Collaborative Watermarking for Adversarial Speech Synthesis
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π Paper: Collaborative Watermarking for Adversarial Speech Synthesis
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π Paper: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
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π Paper: Automatic Prosody Annotation with Pre-Trained Text-Speech Model
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π Paper: Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models
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π Paper: YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
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π Paper: PortaSpeech: Portable and High-Quality Generative Text-to-Speech
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π Paper: Big Bird: Transformers for Longer Sequences
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π Paper: wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
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π Paper: Glow-TTS: A Generative Flow for Text-to-Speech Synthesis
π Summary: This paper introduces a flow-based model for TTS, improving robustness compared to Tacotron.
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π Paper: Survey on Deep Neural Networks in Speech and Vision Systems
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π Paper: vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
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π Paper: wav2vec: Unsupervised Pre-training for Speech Recognition
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π Paper: AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
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π Paper: Glow: Generative Flow with Invertible 1x1 Convolutions
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π Paper: Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation