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Found 5 papers, 1 papers with code
Non-Intrusive Speech Intelligibility Prediction for Hearing-Impaired Users using Intermediate ASR Features and Human Memory Models
Neural networks have been successfully used for non-intrusive speech intelligibility prediction.
The Effect of Spoken Language on Speech Enhancement using Self-Supervised Speech Representation Loss Functions
In this work, SE models are trained and tested on a number of different languages, with self-supervised representations which themselves are trained using different language combinations and with differing network structures as loss function representations.
Non Intrusive Intelligibility Predictor for Hearing Impaired Individuals using Self Supervised Speech Representations
Self-supervised speech representations (SSSRs) have been successfully applied to a number of speech-processing tasks, e. g. as feature extractor for speech quality (SQ) prediction, which is, in turn, relevant for assessment and training speech enhancement systems for users with normal or impaired hearing.
Perceive and predict: self-supervised speech representation based loss functions for speech enhancement
Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models.
MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data
Training of speech enhancement systems often does not incorporate knowledge of human perception and thus can lead to unnatural sounding results.
