Book Chapter10.1007/978-3-642-38865-1_47
Automatic Speech Segmentation for Automatic Speech Translation
Piotr Kłosowski,Adam Dustor +1 more
12
TL;DR: The article presents selected, effective speech signal processing algorithms and their use in order to improve the automatic speech translation.
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Abstract: The article presents selected, effective speech signal processing algorithms and their use in order to improve the automatic speech translation. Automatic speech translation uses natural language processing techniques implemented using algorithms of automatic speech recognition, speaker recognition, automatic text translation and text-to-speech synthesis. It is very possible to improve the process of automatic speech translation by using effective algorithms for automatic segmentation of speech signals based on speaker recognition and language recognition.
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Citations
Deep Learning for Natural Language Processing and Language Modelling
Piotr Kłosowski
- 01 Sep 2018
TL;DR: One of the methods of effective language modelling with the use of deep learning techniques is presented in this paper, which concerns the modelling of the Polish language and can also be applied to language modelling application for other languages.
70
Statistical analysis of orthographic and phonemic language corpus for word-based and phoneme-based Polish language modelling
TL;DR: Results from performed statistical analysis of Polish language statistical analysis enable to develop statistical word-based and phoneme-based language models for Polish, which can effectively contribute to efficiency improvement of automatic speech recognition for Polish.
Automatic syllable segmentation algorithm of Chinese speech based on MF-DFA
TL;DR: The experimental results indicated that the multi fractal characteristics based on MF-DFA possess good distinction and robustness, and the proposed algorithm outperforms the earlier approaches in terms of the performance of Chinese syllable segmentation even in low SNR.
10
Speaker recognition system with good generalization properties
Adam Dustor,Piotr Kłosowski,Jacek Izydorczyk +2 more
- 14 Apr 2014
TL;DR: This paper presents speaker recognition system possessing very good generalization properties, and achieves relatively low equal error rate for speaker verification and high identification rate for identification for very short training and testing sequences.
9
Speech Recognition Based on Open Source Speech Processing Software
TL;DR: The article presents the possibility of using open source speech processing software to construct own speech recognition application by using frameworks based on open sourcespeech processing software.
9
References
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Fundamentals of speech recognition
Lawrence R. Rabiner,Biing-Hwang Juang +1 more
- 01 Jan 1993
TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
9.4K
Robust text-independent speaker identification using Gaussian mixture speaker models
Douglas A. Reynolds,Richard Rose +1 more
TL;DR: The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task.
3.3K
•Book
Statistical Machine Translation
Philipp Koehn
- 18 Jan 2010
TL;DR: This introductory text to statistical machine translation (SMT) provides all of the theories and methods needed to build a statistical machine translator, such as Google Language Tools and Babelfish, and the companion website provides open-source corpora and tool-kits.
Man-machine interactions
Krzysztof A. Cyran,Stanisław Kozielski,James F. Peters,Urszula Stańczyk,Alicja Wakulicz-Deja +4 more
- 01 Jan 2009
TL;DR: Speech Man-Machine Communication Stochastic Effects in Signaling Pathways in Cells: Interaction between Visualization and Modeling and Rough-Granular Computing in Human-Centric Information Processing.
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