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Deep Neural Networks for Automatic Speaker Recognition Do Not Learn Supra-Segmental Temporal Features
While deep neural networks have shown impressive results in automatic speaker recognition and related tasks, it is dissatisfactory how little is understood about what exactly is responsible for these results.
Reorganization of the auditory-perceptual space across the human vocal range
We analyzed the auditory-perceptual space across a substantial portion of the human vocal range (220-1046 Hz) using multidimensional scaling analysis of cochlea-scaled spectra from 250-ms vowel segments, initially studied in Friedrichs et al. (2017) J. Acoust.
Arabic Speech Rhythm Corpus: Read and Spontaneous Speaking Styles
The database serves as a phonetic resource, which allows researchers to examine various aspects of Arabic supra-segmental features and it can be used for forensic phonetic research, for comparison of different speakers, analyzing variability in different speaking styles, and automatic speech and speaker recognition.
A Crowdsourcing Smartphone Application for Swiss German: Putting Language Documentation in the Hands of the Users
Besides, the Voice {\~A}pp takes its users on a journey in which they explore the individuality of their own voices, answering questions such as: How high is my voice?
