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Found 10 papers, 3 papers with code
Mongolian Questions Classification Based on Mulit-Head Attention
This paper proposes a classification model, which combines the Bi-LSTM model with the Multi-Head Attention mechanism.
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.
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.
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.
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.
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.
