Autor: Korzinek, D. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: System for Automatic Transcription of Sessions of the Polish Senate
Autorzy:: Marasek, K.
Koržinek, D.
Brocki, Ł.
Powiązania:: https://bibliotekanauki.pl/articles/176798.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: large vocabulary speech recognition
language modeling
transcription
transliteration
subtitles
Opis:: This paper describes research behind a Large-Vocabulary Continuous Speech Recognition (LVCSR) system for the transcription of Senate speeches for the Polish language. The system utilizes several components: a phonetic transcription system, language and acoustic model training systems, a Voice Activity Detector (VAD), a LVCSR decoder, and a subtitle generator and presentation system. Some of the modules relied on already available tools and some had to be made from the beginning but the authors ensured that they used the most advanced techniques they had available at the time. Finally, several experiments were performed to compare the performance of both more modern and more conventional technologies.
Źródło:: Archives of Acoustics; 2014, 39, 4; 501-509
0137-5075
Pojawia się w:: Archives of Acoustics
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

Skocz do pozycji: 3.

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

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