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Found 17 papers, 3 papers with code
End-to-End Transformer-based Automatic Speech Recognition for Northern Kurdish: A Pioneering Approach
Automatic Speech Recognition (ASR) for low-resource languages remains a challenging task due to limited training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Advanced Clustering Techniques for Speech Signal Enhancement: A Review and Metanalysis of Fuzzy C-Means, K-Means, and Kernel Fuzzy C-Means Methods
Speech signal processing is a cornerstone of modern communication technologies, tasked with improving the clarity and comprehensibility of audio data in noisy environments.
Enhancing Kurdish Text-to-Speech with Native Corpus Training: A High-Quality WaveGlow Vocoder Approach
In this paper, we improve the Kurdish TTS system based on Tacotron by training the Kurdish WaveGlow vocoder on a 21-hour central Kurdish speech corpus instead of using a pre-trained English vocoder WaveGlow.
Breaking Walls: Pioneering Automatic Speech Recognition for Central Kurdish: End-to-End Transformer Paradigm
The fine-tuned xls-r-2b model, combined with a 3-gram language model and included external Kurdish tokenizer, achieved the best performance, yielding a Word Error Rate (WER) of 10. 0% on the validation set and 11. 8% on the Asosoft test set.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility Assessment
Dysarthria is a disability that causes a disturbance in the human speech system and reduces the quality and intelligibility of a person's speech.
Adjustable Privacy using Autoencoder-based Learning Structure
Inference centers need more data to have a more comprehensive and beneficial learning model, and for this purpose, they need to collect data from data providers.
Fake news detection using parallel BERT deep neural networks
There are several approaches to solving this problem, one of which is to detect fake news based on its text style using deep neural networks.
CKMorph: A Comprehensive Morphological Analyzer for Central Kurdish
In order to provide a benchmark for future research, we collected, manually labeled, and publicly shared test sets for evaluating accuracy and coverage of the analyzer.
Persian Rhetorical Structure Theory
Over the past years, interest in discourse analysis and discourse parsing has steadily grown, and many discourse-annotated corpora and, as a result, discourse parsers have been built.
Automatic Meter Classification of Kurdish Poems
As the vowel length is not phonemic in the language, there are uncertainties in syllable weight and meter identification.
Jira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon
To fill this gap, we introduce the first speech corpus and pronunciation lexicon for the Kurdish language.
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows
An autoencoder network is used for nonlinear dimension reduction and feature extraction as an alternative for singular value decomposition (SVD).
Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network
To embed document relevance in topics in the distributed representation, we use a Siamese neural network to jointly learn document representations.
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications
Besides, exploration and exploiting of semantic relations is regarded as a principal step in text mining applications.
Deep Learning Approach on Information Diffusion in Heterogeneous Networks
In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model.
Native Language Identification Using a Mixture of Character and Word N-grams
Native language identification (NLI) is the task of determining an author{'}s native language, based on a piece of his/her writing in a second language.
Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering
Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets.
