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nnSpeech: Speaker-Guided Conditional Variational Autoencoder for Zero-Shot Multi-speaker text-to-speech
Multi-speaker text-to-speech (TTS) using a few adaption data is a challenge in practical applications. To address that, we propose a …
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nnSpeech: Speaker-Guided Conditional Variational Autoencoder for Zero-Shot Multi-speaker text-to-speech
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition
The Transformer architecture model, based on self-attention and multi-head attention, has achieved remarkable success in offline …
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DT-SV: A Transformer-based Time-domain Approach for Speaker Verification
Speaker verification (SV) aims to determine whether the speaker’s identity of a test utterance is the same as the reference …
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Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation
Unsupervised representation learning for speech audios attained impressive performances for speech recognition tasks, particularly when …
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QSpeech: Low-Qubit Quantum Speech Application Toolkit
Quantum devices with low qubits are common in the Noisy Intermediate-Scale Quantum (NISQ) era. However, Quantum Neural Network (QNN) …
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r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled Noise Introducing and Contextual Information Incorporation
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Towards Speaker Age Estimation With Label Distribution Learning
Existing methods for speaker age estimation usually treat it as a multi-class classification or a regression problem. However, precise …
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CycleGEAN: Cycle Generative Enhanced Adversarial Network for Voice Conversion
Cycle Generative Adversarial Network (CycleGAN) for voice conversion (VC) task only used discriminators to identify whether the input …
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CycleGEAN: Cycle Generative Enhanced Adversarial Network for Voice Conversion
Reconstructing Dual Learning for Neural Voice Conversion Using Relatively Few Samples
This paper introduces a dual learning system for neural voice conversion (DualVC) using relatively few samples based on the symmetry of …
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Reconstructing Dual Learning for Neural Voice Conversion Using Relatively Few Samples
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Recently, AutoVC, a …
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TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training