Abstract :- When listening in noise, speech intelligibility fades time-to-time. To ensure the effortless listening, one must modify the speech acoustic cues to be stronger under noise masking. Conventional signal processing approach (SigPro) has been widely used for the task, But it fails to mitigate the background noise effect at the recording end (far-end). To address this, We proposed the neural network approach (NeuralNet) that can boost perform the intelligibility modification of speech recorded even in inferior acoustic condition.
Few samples from the two models in quite listening condition
Listen the same samples under speech shaped noise (SSN) at -7dB level
Acknowledgment: This work was funded by the E.U. Horizon2020 Grant Agreement 675324, Marie Sklodowska-Curie Innovative Training Network, ENRICH.