NISQA - Non-Intrusive Speech Quality and TTS Naturalness Assessment
-
Updated
Dec 1, 2024 - Python
NISQA - Non-Intrusive Speech Quality and TTS Naturalness Assessment
Python implementation of performance metrics in Loizou's Speech Enhancement book
VoIP signaling and media test automation
Computes the Mel-Cepstral Distance of two WAV files based on the paper "Mel-Cepstral Distance Measure for Objective Speech Quality Assessment" by Robert F. Kubichek.
A toolkit to calculate speech audio quality. Not affiliated with the original authors
Deep Noise Suppression for Real Time Speech Enhancement in a Single Channel Wide Band Scenario
Objective measures of speech quality SNR
Go baresip wrapper for automated SIP tests
Implementations of audio watermarking methods, speech quality metrics and attacks in different domains.
Python implementation of a few speech intelligibility prediction algorithms
Train no-reference speech quality estimators with multiple datasets via learned, per-dataset alignments.
Bias-Aware Loss for Training Image and Speech Quality Prediction Models from Multiple Dataset
This repository belongs to my Bachelor's thesis on predicting voice likability from pre-trained speech embeddings.
Know the quality of your speech
Dataset of crowdsourced Speech Quality Assessment using the Comparison Category Rating (CCR) test method
Add a description, image, and links to the speech-quality topic page so that developers can more easily learn about it.
To associate your repository with the speech-quality topic, visit your repo's landing page and select "manage topics."