no code implementations • CoNLL (EMNLP) 2021 • Siddique Latif, Inyoung Kim, Ioan Calapodescu, Laurent Besacier
In this paper, we investigate whether we can control prosody directly from the input text, in order to code information related to contrastive focus which emphasizes a specific word that is contrary to the presuppositions of the interlocutor.
no code implementations • 21 Mar 2023 • Siddique Latif, Aun Zaidi, Heriberto Cuayahuitl, Fahad Shamshad, Moazzam Shoukat, Junaid Qadir
The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences.
no code implementations • 30 Oct 2022 • Muhammad Usman, Azka Rehman, Abdullah Shahid, Siddique Latif, Shi Sub Byon, Byoung Dai Lee, Sung Hyun Kim, Byung il Lee, Yeong Gil Shin
Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i. e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net).
no code implementations • 1 Jan 2021 • Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria
We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain.
no code implementations • 2 Jan 2020 • Siddique Latif, Rajib Rana, Sara Khalifa, Raja Jurdak, Junaid Qadir, Björn W. Schuller
Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 24 Oct 2019 • Thejan Rajapakshe, Rajib Rana, Siddique Latif, Sara Khalifa, Björn W. Schuller
Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 18 Apr 2019 • Kazi Nazmul Haque, Siddique Latif, Rajib Rana
Learning disentangled representation from any unlabelled data is a non-trivial problem.
no code implementations • 20 Feb 2019 • Muhammad Usman, Muhammad Umar Farooq, Siddique Latif, Muhammad Asim, Junaid Qadir
The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis.
1 code implementation • 15 Dec 2018 • Siddique Latif, Adnan Qayyum, Muhammad Usman, Junaid Qadir
Cross-lingual speech emotion recognition is an important task for practical applications.
no code implementations • 28 Nov 2018 • Siddique Latif, Rajib Rana, Junaid Qadir
Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems.
no code implementations • 24 Nov 2018 • Siddique Latif, Muhammad Asim, Muhammad Usman, Junaid Qadir, Rajib Rana
Multishot Magnetic Resonance Imaging (MRI) has recently gained popularity as it accelerates the MRI data acquisition process without compromising the quality of final MR image.
no code implementations • 25 Jan 2018 • Siddique Latif, Muhammad Usman, Rajib Rana, Junaid Qadir
Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise.
no code implementations • 25 Jan 2018 • Muhammad Usman, Siddique Latif, Junaid Qadir
Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature.
Dimensionality Reduction Facial Expression Recognition (FER)
no code implementations • 25 Jan 2018 • Muhammad Atif, Siddique Latif, Rizwan Ahmad, Adnan Khalid Kiani, Junaid Qadir, Adeel Baig, Hisao Ishibuchi, Waseem Abbas
Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements.
1 code implementation • 19 Jan 2018 • Siddique Latif, Rajib Rana, Shahzad Younis, Junaid Qadir, Julien Epps
The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions.
no code implementations • 23 Dec 2017 • Siddique Latif, Rajib Rana, Junaid Qadir, Julien Epps
Inspired by this, we propose VAEs for deriving the latent representation of speech signals and use this representation to classify emotions.