Wszystkie pola: Korzinek, D. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Comparison and adaptation of automatic evaluation metrics for quality assessment of re-speaking
Autorzy:: Wołk, K.
Korzinek, D.
Powiązania:: https://bibliotekanauki.pl/articles/952950.pdf
Data publikacji:: 2017
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: speech
re-speaking
machine translation
evaluation
Opis:: Re-speaking is a mechanism for obtaining high-quality subtitles for use in live broadcasts and other public events. Because it relies on humans to perform the actual re-speaking, the task of estimating the quality of the results is non- trivial. Most organizations rely on human effort to perform the actual quality assessment, but purely automatic methods have been developed for other similar problems (like Machine Translation). This paper will try to compare several of these methods: BLEU, EBLEU, NIST, METEOR, METEOR-PL, TER, and RIBES. These will then be matched to the human-derived NER metric, commonly used in re-speaking. The purpose of this paper is to assess whether the above automatic metrics normally used for MT system evaluation can be used in lieu of the manual NER metric to evaluate re-speaking transcripts.
Źródło:: Computer Science; 2017, 18 (2); 129-144
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Pre-trained deep neural network using sparse autoencoders and scattering wavelet transform for musical genre recognition
Autorzy:: Kleć, M.
Korzinek, D.
Powiązania:: https://bibliotekanauki.pl/articles/952940.pdf
Data publikacji:: 2015
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: Sparse Autoencoders
deep learning
genre recognition
Scattering Wavelet Transform
Opis:: Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scatter- Ing Wavelet Transform (SWT) for classifying musical genres. The SWT uses A sequence of Wavelet Transforms to compute the modulation spectrum coef- Ficients of multiple orders, which has already shown to be promising for this Task. The DNN in this work uses pre-trained layers using Sparse Autoencoders (SAE). Data obtained from the Creative Commons website jamendo.com is Used to boost the well-known GTZAN database, which is a standard bench- mark for this task. The final classifier is tested using a 10-fold cross validation To achieve results similar to other state-of-the-art approaches.
Źródło:: Computer Science; 2015, 16 (2); 133-144
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

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