2 citations · 2 across the 2 of their papers we have counts for
9 papers
Using Rater and System Metadata to Explain Variance in the VoiceMOS Challenge 2022 Dataset
Michael Chinen, Jan Skoglund, Chandan K A Reddy +2
Non-reference speech quality models are important for a growing number of applications. The VoiceMOS 2022 challenge provided a dataset of synthetic voice conversion and text-to-spe…
WARP-Q: Quality Prediction For Generative Neural Speech Codecs
Wissam A. Jassim, Jan Skoglund, Michael Chinen +1
Good speech quality has been achieved using waveform matching and parametric reconstruction coders. Recently developed very low bit rate generative codecs can reconstruct high qual…
Generative Speech Coding with Predictive Variance Regularization
W. Bastiaan Kleijn, Andrew Storus, Michael Chinen +5
The recent emergence of machine-learning based generative models for speech suggests a significant reduction in bit rate for speech codecs is possible. However, the performance of…
ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric
Michael Chinen, Felicia S. C. Lim, Jan Skoglund +3
Estimation of perceptual quality in audio and speech is possible using a variety of methods. The combined v3 release of ViSQOL and ViSQOLAudio (for speech and audio, respectively,)…
Speech Quality Factors for Traditional and Neural-Based Low Bit Rate Vocoders
Wissam A. Jassim, Jan Skoglund, Michael Chinen +1
This study compares the performances of different algorithms for coding speech at low bit rates. In addition to widely deployed traditional vocoders, a selection of recently develo…
Generative Speech Enhancement Based on Cloned Networks
Michael Chinen, W. Bastiaan Kleijn, Felicia S. C. Lim +1
We propose to implement speech enhancement by the regeneration of clean speech from a salient representation extracted from the noisy signal. The network that extracts salient feat…