Search Results for author:
Found 24 papers, 2 papers with code
Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification
We borrow the idea of neural architecture search(NAS) for the textindependent speaker verification task.
Neural Architecture Search
Text-Independent Speaker Verification
Enhancing Data-Free Adversarial Distillation with Activation Regularization and Virtual Interpolation
We add an activation regularizer and a virtual interpolation method to improve the data generation efficiency.
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.
Speech2Video: Cross-Modal Distillation for Speech to Video Generation
This paper investigates a novel task of talking face video generation solely from speeches.
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
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.
Leveraging Causal Inference for Explainable Automatic Program Repair
The experiments on four programming languages (Java, C, Python, and JavaScript) show that CPR can generate causal graphs for reasonable interpretations and boost the performance of bug fixing in automatic program repair.
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.
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator
Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs have been widely applied in many scenarios.
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
Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation
Buddhism is an influential religion with a long-standing history and profound philosophy.
Pose Guided Human Image Synthesis with Partially Decoupled GAN
Most existing methods encode the texture of the whole reference human image into a latent space, and then utilize a decoder to synthesize the image texture of the target pose.
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.
Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification
Data-Free Knowledge Distillation (DFKD) has recently attracted growing attention in the academic community, especially with major breakthroughs in computer vision.
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing
Out-of-distribution (OOD) detection aims at enhancing standard deep neural networks to distinguish anomalous inputs from original training data.
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer
To address local forgetting caused by new classes of new tasks and global forgetting brought by non-i. i. d (non-independent and identically distributed) class imbalance across different local clients, we proposed an Enhancer distillation method to modify the imbalance between old and new knowledge and repair the non-i. i. d.
Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning
This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes.
EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem.
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
This perspective is motivated by our observation that gradient-based attribution methods encounter challenges in assigning feature importance to OOD data, thereby yielding divergent explanation patterns.
P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer
Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance degradation when these agents face new tasks.
INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning
This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting.
Value-Driven Mixed-Precision Quantization for Patch-Based Inference on Microcontrollers
In addition, for patches without outlier values, we utilize value-driven quantization search (VDQS) on the feature maps of their following dataflow branches to reduce search time.
Retrieval-Augmented Audio Deepfake Detection
With recent advances in speech synthesis including text-to-speech (TTS) and voice conversion (VC) systems enabling the generation of ultra-realistic audio deepfakes, there is growing concern about their potential misuse.
