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| rdf:type
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| Description
| - This contribution is focused on a part of data pre-processin - an automatic segmentation (labeling) of utterances into phonemes. Correct and accurate segmentation is the most important part of speech signal pre-processing for analysis, recognition and speech synthesis. This part is very time-consuming because the current speech corpuses are large. Therefore an automation of this process is very necessary. The artificial neural network (ANN) application is our contribution to solving this problem. We have focused on two types of neural nets - multilayer neural networks (MLNN) with BPG algorithm and Kohonen's self-organizing maps. The basic idea of our first experiment with MLNN was a creation of 36 ANN. Each of these nets was trained for one phoneme of the Czech language to classify it. The output from ANN was determined by a similarity rate of input data to trained data. Next experiments were focused on Kohonen's maps.
- This contribution is focused on a part of data pre-processin - an automatic segmentation (labeling) of utterances into phonemes. Correct and accurate segmentation is the most important part of speech signal pre-processing for analysis, recognition and speech synthesis. This part is very time-consuming because the current speech corpuses are large. Therefore an automation of this process is very necessary. The artificial neural network (ANN) application is our contribution to solving this problem. We have focused on two types of neural nets - multilayer neural networks (MLNN) with BPG algorithm and Kohonen's self-organizing maps. The basic idea of our first experiment with MLNN was a creation of 36 ANN. Each of these nets was trained for one phoneme of the Czech language to classify it. The output from ANN was determined by a similarity rate of input data to trained data. Next experiments were focused on Kohonen's maps. (en)
- Příspěvek je zaměřen na část automatické segmentace promluv na fonémy. Je to součást předzpracování dat. Korektní a přesná segmentace je nejdůležitější část předzpracování řeči pro účely analýzy, rozpoznání a syntézy.Je časově velmi náročná, zejména u rozsáhlých databází. Proto je nutné proces zautomatizovat. UNS aplikace je naším příspěvkem do této oblasti. Soustředili jsme se na dva druhy UNS, vícevrstvou neuronovou síť s BPG algoritmem a na Kohonenovu samoorganizující se mapu. První experimenty s MLNN byly založeny na vytvoření 36 UNS, z nichž každá byla trénována na jeden český foném. Výstup byl určen pomocí koeficientu podobnosti vstupních dat s natrénovanou sítí. Další experimenty budou soustředěny na užití Kohonenových map. (cs)
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| Title
| - Automatic Segmentation of the Speech Signal by Artificial Neural Networks.
- Automatická segmentace řečového signálu pomocí umělých neuronových sítí. (cs)
- Automatic Segmentation of the Speech Signal by Artificial Neural Networks. (en)
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| skos:prefLabel
| - Automatic Segmentation of the Speech Signal by Artificial Neural Networks.
- Automatická segmentace řečového signálu pomocí umělých neuronových sítí. (cs)
- Automatic Segmentation of the Speech Signal by Artificial Neural Networks. (en)
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| skos:notation
| - RIV/68407700:21230/07:03135528!RIV08-MZ0-21230___
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| http://linked.open.../vavai/riv/strany
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
| - P(NR8287), Z(MSM6840770012)
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| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/07:03135528
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Neural Networks, Speech, Segmentation (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - ECMS 2007 8-th Int. WSP on Electronics, Control, Modelling, Measurement and Signals
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Tučková, Jana
- Žůrek, Michal
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
| |
| http://linked.open...n/vavai/riv/zamer
| |
| number of pages
| |
| http://purl.org/ne...btex#hasPublisher
| - Technická univerzita v Liberci
|
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
| is http://linked.open...avai/riv/vysledek
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