ﻻ يوجد ملخص باللغة العربية
Disease-modifying treatments are currently assessed in neurodegenerative diseases. Huntingtons Disease represents a unique opportunity to design automatic sub-clinical markers, even in premanifest gene carriers. We investigated phonatory impairments as potential clinical markers and propose them for both diagnosis and gene carriers follow-up. We used two sets of features: Phonatory features and Modulation Power Spectrum Features. We found that phonation is not sufficient for the identification of sub-clinical disorders of premanifest gene carriers. According to our regression results, Phonatory features are suitable for the predictions of clinical performance in Huntingtons Disease.
Phonation, or the vibration of the vocal folds, is the primary source of vocalization in the production of voiced sounds by humans. It is a complex bio-mechanical process that is highly sensitive to changes in the speakers respiratory parameters. Sin
We propose a flexible framework that deals with both singer conversion and singers vocal technique conversion. The proposed model is trained on non-parallel corpora, accommodates many-to-many conversion, and leverages recent advances of variational a
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis. First, we predict fu
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE18 also m
Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because of the end-to-end training, an AED model is usually trained with speech-text paired data. It is challenging to incorporate extern