Search Results for author:
Found 10 papers, 3 papers with code
Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition
In this paper, we propose novel stochastic modeling of various components of a continuous sign language recognition (CSLR) system that is based on the transformer encoder and connectionist temporal classification (CTC).
Natural Language-Assisted Sign Language Recognition
Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth.
Ranked #1 on
Sign Language Recognition
on WLASL-2000
On the Audio-visual Synchronization for Lip-to-Speech Synthesis
Most lip-to-speech (LTS) synthesis models are trained and evaluated under the assumption that the audio-video pairs in the dataset are perfectly synchronized.
Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal
We name the CSLR model trained with the above auxiliary tasks as consistency-enhanced CSLR, which performs well on signer-dependent datasets in which all signers appear during both training and testing.
Ranked #7 on
Sign Language Recognition
on CSL-Daily
Two-Stream Network for Sign Language Recognition and Translation
RGB videos, however, are raw signals with substantial visual redundancy, leading the encoder to overlook the key information for sign language understanding.
C2SLR: Consistency-Enhanced Continuous Sign Language Recognition
The backbone of most deep-learning-based continuous sign language recognition (CSLR) models consists of a visual module, a sequential module, and an alignment module.
Transformer based Multilingual document Embedding model
One of the current state-of-the-art multilingual document embedding model LASER is based on the bidirectional LSTM neural machine translation model.
Cross-lingual Multi-speaker Text-to-speech Synthesis for Voice Cloning without Using Parallel Corpus for Unseen Speakers
Speaker similarity is good for native speech from native speakers.
NMT-based Cross-lingual Document Embeddings
This paper further adds a distance constraint to the training objective function of NV so that the two embeddings of a parallel document are required to be as close as possible.
Cross-Lingual Document Classification
Document Classification
+6
Derivation of Document Vectors from Adaptation of LSTM Language Model
In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector.
