NISQA - Non-Intrusive Speech Quality and TTS Naturalness Assessment
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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
Train no-reference speech quality estimators with multiple datasets via learned, per-dataset alignments.
Implementations of audio watermarking methods, speech quality metrics and attacks in different domains.
Go baresip wrapper for automated SIP tests
Python implementation of a few speech intelligibility prediction algorithms
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
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