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Found 18 papers, 2 papers with code
KADO@LT-EDI-ACL2022: BERT-based Ensembles for Detecting Signs of Depression from Social Media Text
Depression is a common and serious mental illness that early detection can improve the patient’s symptoms and make depression easier to treat.
Leveraging Visemes for Better Visual Speech Representation and Lip Reading
We evaluate our approach on various tasks, including word-level and sentence-level lip reading, and audiovisual speech recognition using the Arman-AV dataset, a largescale Persian corpus.
Word-level Persian Lipreading Dataset
Lip-reading has made impressive progress in recent years, driven by advances in deep learning.
ArmanTTS single-speaker Persian dataset
TTS, or text-to-speech, is a complicated process that can be accomplished through appropriate modeling using deep learning methods.
A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset
In addition, we have proposed a technique to detect visemes (a visual equivalent of a phoneme) in Persian.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+5
ArmanEmo: A Persian Dataset for Text-based Emotion Detection
With the recent proliferation of open textual data on social media platforms, Emotion Detection (ED) from Text has received more attention over the past years.
Lotus at SemEval-2021 Task 2: Combination of BERT and Paraphrasing for English Word Sense Disambiguation
In this paper, we describe our proposed methods for the multilingual word-in-Context disambiguation task in SemEval-2021.
Lip reading using external viseme decoding
Lip-reading is the operation of recognizing speech from lip movements.
Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan
This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021.
A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database
We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR.
BUT System Description for DIHARD Speech Diarization Challenge 2019
This paper describes the systems developed by the BUT team for the four tracks of the second DIHARD speech diarization challenge.
BUT System Description to VoxCeleb Speaker Recognition Challenge 2019
The last two networks are one-dimensional CNN and are based on the x-vector extraction topology.
Detecting Spoofing Attacks Using VGG and SincNet: BUT-Omilia Submission to ASVspoof 2019 Challenge
In this paper, we present the system description of the joint efforts of Brno University of Technology (BUT) and Omilia -- Conversational Intelligence for the ASVSpoof2019 Spoofing and Countermeasures Challenge.
BUT VOiCES 2019 System Description
This is a description of our effort in VOiCES 2019 Speaker Recognition challenge.
Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors
By making use of the natural partition of input features into digits, we train digit-specific i-vector extractors on top of each HMM and we extract well-localized i-vectors, each modelling merely the phonetic content corresponding to a single digit.
How to Improve Your Speaker Embeddings Extractor in Generic Toolkits
Recently, speaker embeddings extracted with deep neural networks became the state-of-the-art method for speaker verification.
Spoken Pass-Phrase Verification in the i-vector Space
The task of spoken pass-phrase verification is to decide whether a test utterance contains the same phrase as given enrollment utterances.
