16 resultados para Employees Training of Thailand
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- ABACUS. Repositorio de Producción Científica - Universidad Europea (1)
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- Comissão Econômica para a América Latina e o Caribe (CEPAL) (14)
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- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (2)
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- Funes: Repositorio digital de documentos en Educación Matemática - Colombia (1)
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- Harvard University (6)
- Helda - Digital Repository of University of Helsinki (17)
- Indian Institute of Science - Bangalore - Índia (11)
- Instituto Nacional de Saúde de Portugal (1)
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- Instituto Superior de Psicologia Aplicada - Lisboa (1)
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- Repositório digital da Fundação Getúlio Vargas - FGV (4)
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- Research Open Access Repository of the University of East London. (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (4)
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- Universidad de Alicante (3)
- Universidad del Rosario, Colombia (9)
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- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (8)
- Universitat de Girona, Spain (2)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (4)
- Université de Lausanne, Switzerland (2)
- Université de Montréal, Canada (6)
- University of Canberra Research Repository - Australia (1)
- University of Michigan (66)
- University of Queensland eSpace - Australia (28)
- University of Washington (1)
- WestminsterResearch - UK (1)
Resumo:
This paper introduces a novel method for the training of a complementary acoustic model with respect to set of given acoustic models. The method is based upon an extension of the Minimum Phone Error (MPE) criterion and aims at producing a model that makes complementary phone errors to those already trained. The technique is therefore called Complementary Phone Error (CPE) training. The method is evaluated using an Arabic large vocabulary continuous speech recognition task. Reductions in word error rate (WER) after combination with a CPE-trained system were obtained with up to 0.7% absolute for a system trained on 172 hours of acoustic data and up to 0.2% absolute for the final system trained on nearly 2000 hours of Arabic data.