In this article a DNN-based system for detection of three common voice disorders (vocal
nodules, polyps and cysts; laryngeal neoplasm; unilateral vocal paralysis) is presented. The …

We participate in the FEMH 2018 Challenge of a bigdata subproject of the IEEE. The goal of
this Challenge is pathological voice detection, and classify the different diseases, including …

We are all exposed to a risk of voice disorders at some point in our life, as we use our voice
daily for speaking, singing, laughing and more. Voice disorders are often characterized by …

This paper describes the participation of TCS Research and Innovation, Mumbai in the
FEMH voice data challenge. The goal of the FEMH voice data challenge is detection of …

Human voice could be significantly affected by neoplasm, vocal palsy, and phono-trauma
diseases. Computer aided diagnosis by analyzing human voice can be a remote and cost …

Computerized detection of voice disorders have attracted considerable academic and
clinical interest in the hope of providing an effective screening method for voice diseases …

The goal of this study is to build an effective model to identify the types of voice disorder,
include Normal, Neoplas, Phonotrauma and Vocal palsy, from FEMH dataset. To deal with …

The goal of the IEEE 2018 FEMH Voice Data Challenge was to develop an effective
algorithmic approach to classifying voice samples as normal or pathological, and further …

The report summarizes the various techniques and feature engineering processes that we
have applied for the Far Eastern Memorial Hospital (FEMH) Voice Data Challenge. We have …

This paper presents the methods and results used by the ByoVoz team for the design of an
automatic voice condition analysis system, which was submitted to the 2018 Far East …