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Novel alignment method for DNN TTS training using HMM synthesis models.

, , , and . SISY, page 271-276. IEEE, (2017)

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Language Model Optimization for a Deep Neural Network Based Speech Recognition System for Serbian., , and . SPECOM, volume 10458 of Lecture Notes in Computer Science, page 483-492. Springer, (2017)A Phonetic Segmentation Procedure Based on Hidden Markov Models., , , , and . SPECOM, volume 9811 of Lecture Notes in Computer Science, page 67-74. Springer, (2016)Novel alignment method for DNN TTS training using HMM synthesis models., , , and . SISY, page 271-276. IEEE, (2017)Speech and Language Resources within Speech Recognition and Synthesis Systems for Serbian and Kindred South Slavic Languages., , , , , , , , and . SPECOM, volume 8113 of Lecture Notes in Computer Science, page 319-326. Springer, (2013)Overcoming Data Sparsity in Automatic Transcription of Dictated Medical Findings., , , , and . EUSIPCO, page 454-458. IEEE, (2022)Using Morphological Data in Language Modeling for Serbian Large Vocabulary Speech Recognition., , and . Comput. Intell. Neurosci., (2019)A robust speaker-independent CPU-based ASR system., , , , and . EUROSPEECH, page 2881-2884. ISCA, (1999)A Comparison of Language Model Training Techniques in a Continuous Speech Recognition System for Serbian., , and . SPECOM, volume 11096 of Lecture Notes in Computer Science, page 522-531. Springer, (2018)Automatic Prosody Generation for Serbo-Croatian Speech Synthesis Based on Regression Trees., , and . INTERSPEECH, page 3157-3160. ISCA, (2011)Energy Normalization in Automatic Speech Recognition., , , and . TSD, volume 5246 of Lecture Notes in Computer Science, page 341-347. Springer, (2008)