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Found 23 papers, 10 papers with code
Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding
Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task.
L$^2$GC: Lorentzian Linear Graph Convolutional Networks For Node Classification
Specifically, we map the learned features of graph nodes into hyperbolic space, and then perform a Lorentzian linear feature transformation to capture the underlying tree-like structure of data.
Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Realistic Incomplete Data Scenarios
Multimodal emotion recognition (MER) in practical scenarios presents a significant challenge due to the presence of incomplete data, such as missing or noisy data.
TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models.
Ranked #16 on
Link Property Prediction
on ogbl-wikikg2
Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
In this paper, we propose a novel ADD model, termed as M2S-ADD, that attempts to discover audio authenticity cues during the mono-to-stereo conversion process.
MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset
Text-to-Speech (TTS) synthesis for low-resource languages is an attractive research issue in academia and industry nowadays.
Explicit Intensity Control for Accented Text-to-speech
Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1).
Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities
Multimodal emotion recognition leverages complementary information across modalities to gain performance.
FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis
Therefore, we propose a novel expressive conversational TTS model, termed as FCTalker, that learn the fine and coarse grained context dependency at the same time during speech generation.
A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion
To answer this question, this paper investigates the utility of these two powerful techniques for CTMBC task combined with agglutinative characteristics of Mongolian language.
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline
This paper introduces a high-quality open-source text-to-speech (TTS) synthesis dataset for Mongolian, a low-resource language spoken by over 10 million people worldwide.
Controllable Accented Text-to-Speech Synthesis
Accented TTS synthesis is challenging as L2 is different from L1 in both in terms of phonetic rendering and prosody pattern.
Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning
In this paper, we propose a data-driven deep learning model, i. e. StrengthNet, to improve the generalization of emotion strength assessment for seen and unseen speech.
Guided Training: A Simple Method for Single-channel Speaker Separation
Another way is to use an anchor speech, a short speech of the target speaker, to model the speaker identity.
Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation
The inner-word encoder uses the self-attention mechanisms to capture the inner-word features of the target word.
Modeling Prosodic Phrasing with Multi-Task Learning in Tacotron-based TTS
We propose a multi-task learning scheme for Tacotron training, that optimizes the system to predict both Mel spectrum and phrase breaks.
An Edge Information and Mask Shrinking Based Image Inpainting Approach
The mask shrinking strategy is employed in the image completion model to track the areas to be repaired.
SNR-Based Teachers-Student Technique for Speech Enhancement
Specifically, this method consists of multiple teacher models and a student model.
Sub-Band Knowledge Distillation Framework for Speech Enhancement
In single-channel speech enhancement, methods based on full-band spectral features have been widely studied.
WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss
To address this problem, we propose a new training scheme for Tacotron-based TTS, referred to as WaveTTS, that has 2 loss functions: 1) time-domain loss, denoted as the waveform loss, that measures the distortion between the natural and generated waveform; and 2) frequency-domain loss, that measures the Mel-scale acoustic feature loss between the natural and generated acoustic features.
Teacher-Student Training for Robust Tacotron-based TTS
We first train a Tacotron2-based TTS model by always providing natural speech frames to the decoder, that serves as a teacher model.
A LSTM Approach with Sub-Word Embeddings for Mongolian Phrase Break Prediction
In this paper, we first utilize the word embedding that focuses on sub-word units to the Mongolian Phrase Break (PB) prediction task by using Long-Short-Term-Memory (LSTM) model.
Mongolian Named Entity Recognition System with Rich Features
The system based on segmenting suffixes with all proposed features yields benchmark result of F-measure=84. 65 on this corpus.
