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ASR performance prediction on unseen broadcast programs using convolutional neurol networks
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In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; https://hal.archives-ouvertes.fr/hal-01709779 ; IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, Alberta, Canada (2018)
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| 2 |
Modelling Semantic Context of OOV Words in Large Vocabulary Continuous Speech Recognition
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In: ISSN: 2329-9290 ; EISSN: 2329-9304 ; IEEE/ACM Transactions on Audio, Speech and Language Processing ; https://hal.inria.fr/hal-01461617 ; IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2017, 25 (3), pp.598 - 610. ⟨10.1109/TASLP.2017.2651361⟩ (2017)
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Modélisation acoustico-phonétique de langues peu dotées : Études phonétiques et travaux de reconnaissance automatique en luxembourgois
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In: Journées d'Etude sur la Parole ; https://hal.archives-ouvertes.fr/hal-01843399 ; Journées d'Etude sur la Parole, Jan 2014, Le Mans, France (2014)
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Speech Alignment and Recognition Experiments for Luxembourgish
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In: Proceedings of the 4th International Workshop on Spoken Language Technologies for Underresourced Languages ; 4th International Workshop on Spoken Language Technologies for Underresourced Languages ; https://hal.archives-ouvertes.fr/hal-01134824 ; 4th International Workshop on Spoken Language Technologies for Underresourced Languages, May 2014, Saint-Petersbourg, Russia. pp.53-60 ; http://www.mica.edu.vn/sltu2014/ (2014)
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Some issues affecting the transcription of hungarian broadcast audio
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In: Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-01843430 ; Annual Conference of the International Speech Communication Association , Aug 2013, Lyon, France (2013)
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| 6 |
Pronunciation modeling for large vocabulary speech recognition
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Abstract:
The large pronunciation variability of words in conversational speech is one of the major causes of low accuracy in automatic speech recognition (ASR). Many pronunciation modeling approaches have been developed to address this problem. Some explicitly manipulate the pronunciation dictionary as well as the set of the units used to define the pronunciations of words. Other approaches model the pronunciation implicitly by using long duration acoustical context to more accurately classify the spoken pronunciation unit. This thesis is a study of the relative ability of the acoustic and the pronunciation models to capture pronunciation variability in a nearly state of the art conversational telephone speech recognizer. Several methods are tested, each designed to improve the modeling accuracy of the recognizer. Some of the experiments result in a lower word error rate, but many do not, apparently because, in different ways, the accuracy gained by one part of the recognizer comes at the expense of accuracy lost or transferred from another part of the recognizer. Pronunciation variability is modeled with two approaches: from above with explicit pronunciation modeling and from below with implicit pronunciation modeling within the acoustic model. Both approaches make use of long duration context, explicitly by considering long-duration pronunciation units and implicitly by having the acoustic model consider long-duration speech segments. Some pronunciation models address the pronunciation variability problem by introducing multiple pronunciations per word to cover more variants observed in conversational speech. However, this can potentially increase the confusability between words. This thesis studies the relationship between pronunciation perplexity and the lexical ambiguity, which has informed the design of the explicit pronunciation models presented here.
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Keyword:
automatic speech recognition (ASR); Conversational speech recognition; Large-Vocabulary Continuous Speech Recognition (LVCSR); Pronunciation modeling
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URL: http://hdl.handle.net/2142/18276
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| 7 |
Stochastic Language Adaptation over Time and State in Natural Spoken Dialogue Systems
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In: http://www.research.att.com/projects/watson/nhmihy/papers/ieee2000.ps (2000)
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| 8 |
Lattice Compression in the Consensual Post-Processing Framework
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In: http://nlp.cs.jhu.edu/~lidia/ISAS99.ps (2000)
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| 9 |
Towards Very Large Vocabulary Word Recognition
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In: DTIC AND NTIS (1982)
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| 10 |
The automation of Directory Assistance Services
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In: http://homepages.inf.ed.ac.uk/kgeorgil/papers/georgila_ijst02.pdf
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| 11 |
Efficient On-The-Fly Hypothesis Rescoring in a Hybrid GPU/CPU-based Large Vocabulary Continuous Speech Recognition Engine
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In: http://www.cs.cmu.edu/~ianlane/publications/2012_Kim_Interspeech.pdf
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| 12 |
ON THE USE OF FRAME-LEVEL INFORMATION CUES FOR MINIMUM PHONE ERROR TRAINING OF ACOUSTIC MODELS
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In: http://berlin.csie.ntnu.edu.tw/Berlin_Research/manuscripts/2006_ntnu_mpe_prior.pdf
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| 13 |
Issues in developing LVCSR System for Dravidian Languages: An exhaustive case study for Tamil
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In: http://research.ijcaonline.org/volume70/number19/pxc3888180.pdf
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