John H. L. Hansen

Semi-supervised Learning with Generative Adversarial Networks for Arabic Dialect Identification

2019

Dialect Identification (DID) refers to the process of identifying different dialects within the same language class. Compared with more general language identification (LID), DID is a more challenging task because of the substantial similarity between …

Advancing Multi-Accented LSTM-CTC Speech Recognition using a Domain Specific Student-Teacher Learning Paradigm

2018 · arXiv (Cornell University)

Non-native speech causes automatic speech recognition systems to degrade in performance. Past strategies to address this challenge have considered model adaptation, accent classification with a model selection, alternate pronunciation lexicon, etc. In this study, we …

Impact of Naturalistic Field Acoustic Environments on Forensic Text-independent Speaker Verification System

2022 · arXiv (Cornell University)

Audio analysis for forensic speaker verification offers unique challenges in system performance due in part to data collected in naturalistic field acoustic environments where location/scenario uncertainty is common in the forensic data collection process. Forensic …

Attention and DCT based Global Context Modeling for Text-independent Speaker Recognition

2022 · arXiv (Cornell University)

Learning an effective speaker representation is crucial for achieving reliable performance in speaker verification tasks. Speech signals are high-dimensional, long, and variable-length sequences containing diverse information at each time-frequency (TF) location. The standard convolutional layer …