Jesper Jensen

TL;DR: A short-time objective intelligibility measure (STOI) is presented, which shows high correlation with the intelligibility of noisy and time-frequency weighted noisy speech (e.g., resulting from noise reduction) of three different listening experiments and showed better correlation with speech intelligibility compared to five other reference objective intelligible models.

TL;DR: An objective intelligibility measure is presented, which shows high correlation (rho=0.95) with the intelligibility of both noisy, and TF-weighted noisy speech, and shows significantly better performance than three other, more sophisticated, objective measures.

TL;DR: In this paper, a permutation invariant training (PIT) was proposed for speaker independent multi-talker speech separation, commonly known as the cocktail-party problem, which minimizes the separation error directly.

TL;DR: In this article, the utterance-level permutation invariant training (uPIT) technique was proposed for speaker independent multitalker speech separation, where RNNs, trained with uPIT, can separate multitalker mixed speech without any prior knowledge of signal duration, number of speakers, speaker identity, or gender.

TL;DR: It is shown that ESTOI can be interpreted in terms of an orthogonal decomposition of short-time spectrograms into intelligibility subspaces, i.e., a ranking of spectrogram features according to their importance to intelligibility.