 Γεια σας! Είμαι ο Ιορδάνι ΣτοΙΤΕΣ, και σήμερα να παρακολουθήσω τη δημιουργία που έχουμε κάνει στην συνεργασία με το ΒαΙΑΣ ΓΕΡΥΝΚ, για τη π성이 ο Μιουλτιτόκιο Εικενοδίου με τη δίπνη Εργασία, ουσχύ grazing. Οι εγώ, καλή αίσθηση, το Μιουλτιτόκιο Εικενοδίου είναι ένα κυρίο της δάσκας, διότι για να παρακολουθεί ο ασφαλήκας but και για να παρακολουθώ ο ασφαλής. Και η πολιτική συνεργασία που ήξερε στοτε το νομό της Ιορδάνιρες και την προστιφία του εργασίας που αντεβήθηκε, δεν πρέπει να σεμηλεί αυτά τα καθιλιά. υπάρχει μόνο σε αυτές τις κατάστασεις. Η κατάστασή για αυτό είναι ότι αφήνουν να αποφασίσουν their ability if they listen to focus on one acoustic source in a noisy environment while ignoring others. Ρίχνωσης, ένας learning-based speech enhancement has a great potential to improve speech intelligibility in the presence of background noise. However, in most research studies, the assessment of speech intelligibility typically involves degradations of the speech signal by background noise or added reverberation. However, in everyday life, acoustic scenes are inherently dynamic with alternations happening and overlapping between speakers that can vary both in level and in timing. To do this, certain ambiguities arise when trying to use different speech processing approaches. For example, speech enhancement, where we have the choice of enhancing the loudest speaker at any time or extract all the available speech content in a make-toot, also with speech separation where the limitation, the main limitation is that the number of speakers and amateurs should be typically estimated or known in advance in order to perform. Also, the iterative procedures can be applied but they're not the best choice for real-time implementations. Here we propose to use target speaker extraction, which is a recently introduced concept that aims to extract the speech of a specific speaker from a mixture given some auxiliary information. We define our research question as to how does target speaker extraction perform in noisy, multi-talker conditions, whether a single model can generalize to various conditions and whether target speaker extraction methods can improve speech perception of normal hearing listeners. We utilize a deep learning model that's based on the dual-bath, recurrent neural network architecture and we modify the last block of this architecture to perform speaker adaptation and fuse the information of speaker embeddings that are captured by a temporal convolutional network from an enrollment at the rest of the target speaker. So, given a noisy multi-speaker mixture and enrollment at the rest of the target speaker, we aim to estimate the target, the voice of the target speaker at the output of the model. The model is trained on noisy mixtures of 1, 2 and 3 speakers on the Librimix dataset and also we remixed the dataset in order to include more challenging and diverse conditions. Also, the enrollment atras were randomly drawn from the dataset and we followed the standard training procedure for speech enhancement models that are based on deep learning. So, here are the results on the clear speech corpus for different parameterizations of the proposed model based on the maximum look ahead time of each model. We can see that all models perform consistently in these conditions and provided improvements of the story intelligibility metric. Also, our model significantly outperforms from the recent state-of-the-art models the time domain speaker beam on the same task. To subjectively evaluate the proposed approach we conduct a listening test with 18 oral hearing participants that are needing speakers of the Greek language and compare the speaker informed deep learning model with a non-informed model and also compare these to the unprocessed mixtures. The models were constrained to a future look ahead time of 5 milliseconds in order for their real-time implementation to be feasible. So, conduct a sentence-accompetition test for 3 conditions with 1, 2 and 3 speakers as you can listen to the following examples. Here are the results for the signal speaker case where the models did not improve work-to-mission scores at 0 dB SNR due to signal effects. In contrast, both models improved work-to-mission scores at minus 3 dB by around 12%. We can see that both models improved work-to-mission scores by around 12%. Also, there was no significant difference between the speaker-uninformed and the speaker-informed model for the single speaker case. We have also observed that it used variants of work-to-mission scores across the listener for the minus 3 dB condition. For the two-speaker condition the proposed speaker-informed TPRNN model improved work-to-revolution scores by 18% while the uninformed model did not improve work-to-mission scores. Also, there was no significant effect of the target interference where we were assessed separately for our conditions. However, we have to note that different genders were always tested first or there might be a presentation bias here. The same behavior is observed for the three-speaker condition where the proposed model improved work-to-informed TPRNNN scores by 16% while the uninformed model did not improve again the work-to-informed TPRNNN scores. In conclusion, we demonstrate that the target-speaker extraction approaches can enhance its perception in noisy multi-token environments over speaker-uninformed approaches in various conditions as we have tested. Also, the target-speaker extraction models can generalize various conditions including different speakers' noises and number of speakers. Also, the proposed model is visible for real-time processing and requires only short utterance in our implementation it was about 2 or 3 seconds and finally, the current study aims to continue to the development of new assistive technologies for improving speed perception in challenging acoustic environments. These are the references of the presentation. Thank you very much for your attention.