activity
20202022
most citedDeep Learning Based Assessment of Synthetic Speech Naturalness

62 citations · 62 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SD2022

ConferencingSpeech 2022 Challenge: Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge for Online Conferencing Applications

Gaoxiong Yi, Wei Xiao, Yiming Xiao +10

With the advances in speech communication systems such as online conferencing applications, we can seamlessly work with people regardless of where they are. However, during online…

eess.AS2021

Full-Reference Speech Quality Estimation with Attentional Siamese Neural Networks

Gabriel Mittags, Sebastian Möller

In this paper, we present a full-reference speech quality prediction model with a deep learning approach. The model determines a feature representation of the reference and the deg…

cs.SD202162 cited

Deep Learning Based Assessment of Synthetic Speech Naturalness

Gabriel Mittag, Sebastian Möller

In this paper, we present a new objective prediction model for synthetic speech naturalness. It can be used to evaluate Text-To-Speech or Voice Conversion systems and works languag…

cs.MM2020

Effect of Language Proficiency on Subjective Evaluation of Noise Suppression Algorithms

Babak Naderi, Gabriel Mittag, Rafael Zequeira Jim\a'enez +1

Speech communication systems based on Voice-over-IP technology are frequently used by native as well as non-native speakers of a target language, e.g. in international phone calls…

eess.AS2020

DNN No-Reference PSTN Speech Quality Prediction

Gabriel Mittag, Ross Cutler, Yasaman Hosseinkashi +4

Classic public switched telephone networks (PSTN) are often a black box for VoIP network providers, as they have no access to performance indicators, such as delay or packet loss.…