NISQA - Non-Intrusive Speech Quality and TTS Naturalness Assessment
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Updated
Mar 8, 2024 - Python
NISQA - Non-Intrusive Speech Quality and TTS Naturalness Assessment
Python implementation of performance metrics in Loizou's Speech Enhancement book
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
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.
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