72 citations · 174 across the 17 of their papers we have counts for
5 papers · 1 filter
End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge
Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch +1
In this paper, we present end-to-end and speech embedding based systems trained in a self-supervised fashion to participate in the ACM Multimedia 2022 ComParE Challenge, specifical…
Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection
Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch +1
The adoption of advanced deep learning (DL) architecture in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets. To this end, this work…
Learnable MFCCs for Speaker Verification
Xuechen Liu, Md Sahidullah, Tomi Kinnunen
We propose a learnable mel-frequency cepstral coefficient (MFCC) frontend architecture for deep neural network (DNN) based automatic speaker verification. Our architecture retains…
Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification
Achintya Kumar Sarkar, Md Sahidullah, Zheng-Hua Tan
In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker ve…
Domain-Dependent Speaker Diarization for the Third DIHARD Challenge
A Kishore Kumar, Shefali Waldekar, Goutam Saha +1
This report presents the system developed by the ABSP Laboratory team for the third DIHARD speech diarization challenge. Our main contribution in this work is to develop a simple a…