Search Results for author:
Found 8 papers, 1 papers with code
3D Face Arbitrary Style Transfer
To solve this problem, we propose a novel method, namely Face-guided Dual Style Transfer (FDST).
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 annotated speech is limited.
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition
For multi-head attention in Transformer ASR, it is not easy to model monotonic alignments in different heads.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DT-SV: A Transformer-based Time-domain Approach for Speaker Verification
Therefore, we propose an approach to derive utterance-level speaker embeddings via a Transformer architecture that uses a novel loss function named diffluence loss to integrate the feature information of different Transformer layers.
QSpeech: Low-Qubit Quantum Speech Application Toolkit
However, Quantum Neural Network (QNN) running on low-qubit quantum devices would be difficult since it is based on Variational Quantum Circuit (VQC), which requires many qubits.
Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection
In this work, we propose a novel task-adaptive module which is easy to plant into any metric-based few-shot learning frameworks.
r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation
In this paper, we aim to evaluate and enhance the robustness of G2P models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Federated Learning with Dynamic Transformer for Text to Speech
Text to speech (TTS) is a crucial task for user interaction, but TTS model training relies on a sizable set of high-quality original datasets.
