2 citations · 4 across the 8 of their papers we have counts for
12 papers
LMCodec: A Low Bitrate Speech Codec With Causal Transformer Models
Teerapat Jenrungrot, Michael Chinen, W. Bastiaan Kleijn +4
We introduce LMCodec, a causal neural speech codec that provides high quality audio at very low bitrates. The backbone of the system is a causal convolutional codec that encodes au…
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…
Ultra-Low-Bitrate Speech Coding with Pretrained Transformers
Ali Siahkoohi, Michael Chinen, Tom Denton +2
Speech coding facilitates the transmission of speech over low-bandwidth networks with minimal distortion. Neural-network based speech codecs have recently demonstrated significant…
A Comparison of Deep Learning MOS Predictors for Speech Synthesis Quality
Alessandro Ragano, Emmanouil Benetos, Michael Chinen +4
Speech synthesis quality prediction has made remarkable progress with the development of supervised and self-supervised learning (SSL) MOS predictors but some aspects related to th…
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…
Salient Speech Representations Based on Cloned Networks
W. Bastiaan Kleijn, Felicia S. C. Lim, Michael Chinen +1
We define salient features as features that are shared by signals that are defined as being equivalent by a system designer. The definition allows the designer to contribute qualit…
Differentiable Consistency Constraints for Improved Deep Speech Enhancement
Scott Wisdom, John R. Hershey, Kevin Wilson +4
In recent years, deep networks have led to dramatic improvements in speech enhancement by framing it as a data-driven pattern recognition problem. In many modern enhancement system…
Exploring Tradeoffs in Models for Low-latency Speech Enhancement
Kevin Wilson, Michael Chinen, Jeremy Thorpe +5
We explore a variety of neural networks configurations for one- and two-channel spectrogram-mask-based speech enhancement. Our best model improves on previous state-of-the-art perf…