Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
Goronzy, Silke.
Robust Adaptation to Non-Native Accents in Automatic Speech Recognition [electronic resource] / by Silke Goronzy. - 1st ed. 2002. - XI, 146 p. online resource. - Lecture Notes in Artificial Intelligence, 2560 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 2560 .
ASR:AnOverview -- Pre-processing of the Speech Data -- Stochastic Modelling of Speech -- Knowledge Bases of an ASR System -- Speaker Adaptation -- Confidence Measures -- Pronunciation Adaptation -- Future Work -- Summary -- Databases and Experimental Settings -- MLLR Results -- Phoneme Inventory.
Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.
9783540362906
10.1007/3-540-36290-8 doi
Artificial intelligence.
Signal processing.
Machine theory.
User interfaces (Computer systems).
Human-computer interaction.
Artificial Intelligence.
Signal, Speech and Image Processing.
Formal Languages and Automata Theory.
User Interfaces and Human Computer Interaction.
Q334-342 TA347.A78
006.3
Robust Adaptation to Non-Native Accents in Automatic Speech Recognition [electronic resource] / by Silke Goronzy. - 1st ed. 2002. - XI, 146 p. online resource. - Lecture Notes in Artificial Intelligence, 2560 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 2560 .
ASR:AnOverview -- Pre-processing of the Speech Data -- Stochastic Modelling of Speech -- Knowledge Bases of an ASR System -- Speaker Adaptation -- Confidence Measures -- Pronunciation Adaptation -- Future Work -- Summary -- Databases and Experimental Settings -- MLLR Results -- Phoneme Inventory.
Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.
9783540362906
10.1007/3-540-36290-8 doi
Artificial intelligence.
Signal processing.
Machine theory.
User interfaces (Computer systems).
Human-computer interaction.
Artificial Intelligence.
Signal, Speech and Image Processing.
Formal Languages and Automata Theory.
User Interfaces and Human Computer Interaction.
Q334-342 TA347.A78
006.3