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This book constitutes revised selected papers from the 6th International Conference on Arabic Language Processing, ICALP 2017, held in Fez, Morocco, in October 2017. The 18 full papers presented in this volume were carefully reviewed and selected from 55 submissions. They were organized in topical sections named: machine translation systems; speech recognition and synthesis; text categorization, clustering and summarization; information retrieval systems; and Arabic NLP tools and applications.
This book constitutes the refereed proceedings of the 15th International Conference of the Pacific Association for Computational Linguistics, PACLING 2017, held in Yangon, Myanmar, in August 2017. The 28 revised full papers presented were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on semantics and semantic analysis; statistical machine translation; corpora and corpus-based language processing; syntax and syntactic analysis; document classification; information extraction and text mining; text summarization; text and message understanding; automatic speech recognition; spoken language and dialogue; speech pathology; speech analysis.
Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field
Automatic speech recognition technology has achieved maturity, where it has been widely integrated into many systems. However, speech recognition system for non-native speakers still suffers from high error rate, which is due to the mismatch between the non-native speech and the trained models. Recording sufficient non-native speech for training is time consuming and often difficult. In this thesis, we propose approaches to adapt acoustic and pronunciation model under different resource constraints for non-native speakers. A preliminary work on accent identification has also been carried out. Multilingual acoustic modeling has been proposed for modeling cross-lingual transfer of non-native speakers to overcome the difficulty in obtaining non-native speech. In cases where multilingual acoustic models are available, a hybrid approach of acoustic interpolation and merging has been proposed for adapting the target acoustic model. The proposed approach has also proven to be useful for context modeling. However, if multilingual corpora are available instead, a class of three interpolation methods has equally been introduced for adaptation. Two of them are supervised speaker adaptation methods, which can be carried out with only few non-native utterances. In term of pronunciation modeling, two existing approaches which model pronunciation variants, one at the pronunciation dictionary and another at the rescoring module have been revisited, so that they can work under limited amount of non-native speech. We have also proposed a speaker clustering approach called "latent pronunciation analysis" for clustering non-native speakers based on pronunciation habits. This approach can also be used for pronunciation adaptation. Finally, a text dependent accent identification method has been proposed. The approach can work with little amount of non-native speech for creating robust accent models. This is made possible with the generalizability of the decision trees and the usage of multilingual resources to increase the performance of the accent models.
This book constitutes the proceedings of the 18th International Conference on Speech and Computer, SPECOM 2016, held in Budapest, Hungary, in August 2016. The 85 papers presented in this volume were carefully reviewed and selected from 154 submissions.
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.