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Found 22 papers, 8 papers with code
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models.
Characteristic-Specific Partial Fine-Tuning for Efficient Emotion and Speaker Adaptation in Codec Language Text-to-Speech Models
To address these challenges, we propose a characteristic-specific partial fine-tuning strategy, short as CSP-FT. First, we use a weighted-sum approach to analyze the contributions of different Transformer layers in a pre-trained codec language TTS model for emotion and speaker control in the generated speech.
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-differentiable 0-1 loss function.
EmoPro: A Prompt Selection Strategy for Emotional Expression in LM-based Speech Synthesis
Recent advancements in speech synthesis models, trained on extensive datasets, have demonstrated remarkable zero-shot capabilities.
VQ-CTAP: Cross-Modal Fine-Grained Sequence Representation Learning for Speech Processing
For tasks such as text-to-speech (TTS), voice conversion (VC), and automatic speech recognition (ASR), a cross-modal fine-grained (frame-level) sequence representation is desired, emphasizing the semantic content of the text modality while de-emphasizing the paralinguistic information of the speech modality.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit Networks
Experimental results on Cifar100 and ImageNet datasets exhibit that \methodname~provides up to a \textbf{50. 00\%} reduction in training time and attains up to a \textbf{6. 94\%} enhancement in accuracy when contrasted with baseline methods across diverse models and tasks.
Learning Pareto Set for Multi-Objective Continuous Robot Control
For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy.
An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios
Self-supervised learning (SSL) representations from massively multilingual models offer a promising solution for low-resource language speech tasks.
Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation
Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles.
Minimize Quantization Output Error with Bias Compensation
Quantization is a promising method that reduces memory usage and computational intensity of Deep Neural Networks (DNNs), but it often leads to significant output error that hinder model deployment.
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
To address this gap, we propose a new metric termed adversarial hypervolume, assessing the robustness of deep learning models comprehensively over a range of perturbation intensities from a multi-objective optimization standpoint.
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks
QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search, and then uses the differentiable neural architecture search (DNAS) algorithm to seek the layer- or model-desired scheme from the set.
Using multiple reference audios and style embedding constraints for speech synthesis
The end-to-end speech synthesis model can directly take an utterance as reference audio, and generate speech from the text with prosody and speaker characteristics similar to the reference audio.
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks
We implement ESB as an accelerator and quantitatively evaluate its efficiency on FPGAs.
Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot
Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility.
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks
Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years.
DiDiSpeech: A Large Scale Mandarin Speech Corpus
This paper introduces a new open-sourced Mandarin speech corpus, called DiDiSpeech.
Audio and Speech Processing
High-precision target positioning system for unmanned vehicles based on binocular vision
In order to verify the effect of the system, this paper collects the accuracy and calculation time of the output results of the cylinder in different poses.
Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis
Accurately modelling the driver behavior at the intersection is essential for intelligent transportation systems (ITS).
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization
Quantization has been proven to be an effective method for reducing the computing and/or storage cost of DNNs.
DELTA: A DEep learning based Language Technology plAtform
In this paper we present DELTA, a deep learning based language technology platform.
Ranked #3 on
Named Entity Recognition
on CoNLL 2003 (English)
Enhanced-alignment Measure for Binary Foreground Map Evaluation
The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways.
