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On recognition of non-native speech using probabilistic lexical model
2014
Interspeech 2014
unpublished
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In this paper, we investigate the role of different factors such as type of lexical model and choice of acoustic units in recognition of speech uttered by non-native speakers. ...
Despite various advances in automatic speech recognition (ASR) technology, recognition of speech uttered by non-native speakers is still a challenging problem. ...
Discussion and Conclusion In this paper, we studied the role of the probabilistic lexical model in the KL-HMM framework in improving non-native speech recognition without using any adaptation data. ...
doi:10.21437/interspeech.2014-6
fatcat:qtudkky7wjg3lnvkbt2xyh2o6a
Integrated pronunciation learning for automatic speech recognition using probabilistic lexical modeling
2015
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Standard automatic speech recognition (ASR) systems use phoneme-based pronunciation lexicon prepared by linguistic experts. ...
In this paper, we propose a graphemebased ASR approach in the framework of probabilistic lexical modeling that integrates pronunciation learning as a stage in ASR system training, and exploits both acoustic ...
We study multi-accent non-native speech recognition using the HIWIRE corpus [20] 2 . ...
doi:10.1109/icassp.2015.7178958
dblp:conf/icassp/RasipuramRM15
fatcat:jedikerrvzeilaay4ma6gl3rgm
On modeling context-dependent clustered states: Comparing HMM/GMM, hybrid HMM/ANN and KL-HMM approaches
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Index Terms-HMM/GMM, hybrid HMM/ANN, Kullback-Leibler divergence based HMM, context-dependent subword units, non-native speech recognition ...
The reduction in number of clustered states has broader implications on model complexity and data sparsity issues. ...
WER on native speech decreases. On the other hand, as number of acoustic states is increased, the WER on non-native speech eventually increases for all the three systems. ...
doi:10.1109/icassp.2014.6855090
dblp:conf/icassp/RazaviRM14
fatcat:4gyzb2hozfhrtmgo4e3pg35xcq
Computer-assisted pronunciation training—Speech synthesis is almost all you need
2022
Speech Communication
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2022 pre-print arXiv:2207.00774v1 fatcat:eg73txiwwre5vjsn7d76s6mbtq
Non-native English speech corpora of German, Italian, and Polish speakers are used in the evaluations. ...
If we had a generative model that could mimic non-native speech and produce any amount of training data, then the task of detecting pronunciation errors would be much easier. ...
[37] use a pair of speech signals from a student and a native speaker to classify native and non-native speech. Mauro et al. ...
doi:10.1016/j.specom.2022.06.003
fatcat:cl7ppxoegngc3bmuc5rzkw63mu
Probabilistic lexical modeling and unsupervised training for zero-resourced ASR
2013
2013 IEEE Workshop on Automatic Speech Recognition and Understanding
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Standard automatic speech recognition (ASR) systems rely on transcribed speech, language models, and pronunciation dictionaries to achieve state-of-the-art performance. ...
In this paper, we propose a novel zero-resourced ASR approach to train acoustic models that only uses list of probable words from the language of interest. ...
. • Non-native speech recognition [9] : Here, the goal was to build ASR system for non-native speech with limited acoustic resources. ...
doi:10.1109/asru.2013.6707771
dblp:conf/asru/RasipuramRM13
fatcat:2myb7plpxzbqpedt2hl6chzjlm
Regularized Speaker Adaptation of KL-HMM for Dysarthric Speech Recognition
2017
IEEE transactions on neural systems and rehabilitation engineering
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Evaluation of the proposed speaker adaptation method on a database of several hundred words for 30 speakers consisting of 12 mildly dysarthric, 8 moderately dysarthric, and 10 non-dysarthric control speakers ...
To capture the phonetic variation, Kullback-Leibler divergence based hidden Markov model (KL-HMM) is adopted, where the emission probability of state is parametrized by a categorical distribution using ...
Acknowledgments This work was supported by the National Research Foundation of Korea (No. 2014r1a2a2a01007650) and the National Institutes of Health (No. r03dc013990). ...
doi:10.1109/tnsre.2017.2681691
pmid:28320669
pmcid:PMC5591083
fatcat:6c2betzlffhydnjfse6sdcl3qi
Speech Perception and Spoken Word Recognition: Past and Present
2002
Ear and Hearing
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spoken word recognition, and research on how infants acquire the capacity to perceive their native language. ...
Our foci in this review fall on three principle topics: early work on the discrimination and categorization of speech sounds, more recent efforts to understand the processes and representations that subserve ...
ACKNOWLEDGMENTS: Preparation of the present manuscript was supported by a Senior Scientist Award from NIMH (01490) and a Research Grant from NICHD (15795) to PWJ and a Research Grant from NIDCD ...
doi:10.1097/00003446-200202000-00002
pmid:11881915
fatcat:2tj5axvwnrglzo2hmijp3z27vq
Multimodal Fusion of Multirate Acoustic, Prosodic, and Lexical Speaker Characteristics for Native Language Identification
2016
Interspeech 2016
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Native language identification from acoustic signals of L2 speakers can be useful in a range of applications such as informing automatic speech recognition (ASR), speaker recognition, and speech biometrics ...
On the classification side we employ SVM, i-Vector, DNN and bottleneck features, and maximum-likelihood models. ...
Identification of the native language (L1) of a non-native English speaker from English (L2) speech is a challenging research problem. ...
doi:10.21437/interspeech.2016-1312
dblp:conf/interspeech/ShivakumarCG16
fatcat:fg7orfwdfbgmdnaqdlbtrlnzia
Acoustic and lexical resource constrained ASR using language-independent acoustic model and language-dependent probabilistic lexical model
2015
Speech Communication
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The potential and the efficacy of the proposed approach is demonstrated through experiments and comparisons with other approaches on three different ASR tasks: non-native and accented speech recognition ...
One of the key challenges involved in building statistical automatic speech recognition (ASR) systems is modeling the relationship between subword units or "lexical units" and acoustic feature observations ...
(a) on the HIWIRE non-native accented speech recognition task, (b) on the Greek ASR task. ...
doi:10.1016/j.specom.2014.12.006
fatcat:hqyp5puyh5brjmatjv44z52ici
Pre-lexical abstraction of speech in the auditory cortex
2009
Trends in Cognitive Sciences
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According to most cognitive models of spoken word recognition, this complexity is dealt with before lexical access via a process of abstraction from the acoustic signal to pre-lexical categories. ...
Recent advances in animal neurophysiology and human functional imaging have made it possible to investigate the processing of speech in terms of probabilistic cortical maps rather than simple cognitive ...
Acknowledgements J.O. is employed by the Max Planck Society, Germany, and received additional funding from the Landesstiftung Baden-Wü rttemberg gGmbH during initial phases of this project. ...
doi:10.1016/j.tics.2008.09.005
pmid:19070534
fatcat:mjfc24ww3benfgz4jm47ii6d2y
Page 486 of Behavior Research Methods Vol. 36, Issue 3
[page]
2004
Behavior Research Methods
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Frequency analysis of English usage: Lexicon and grammar. Boston: Houghton Mifflin.
Gaygen, D. E. (1997). The effects of probabilistic phonotactics on the .segmentation of continuous .speech. ...
The effect of probabilistic phonotactics on lexical acquisition. Clinical Linguistics & Phonetics, 14, 407-425.
Treiman, R. (1986). The division between onsets and rimes in English syllables. ...
Managing Speech Perception Data Sets
[chapter]
2022
The Open Handbook of Linguistic Data Management
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Acknowledgments The order of authorship is alphabetical. The research described in this contribution was financially supported by the Max Planck Society and all authors were previ- ...
The principal stimulus for the project was to provideinput data for a probabilistic model of spoken-word recognition, the SHORTLIST-B model of Dutch spokenword recognition (Norris & McQueen 2008), and ...
Although most citations are in publications on the central issue of native versus non-native speech perception, the study has also been cited on non-nativeness effects at higher levels of linguistic processing ...
doi:10.7551/mitpress/12200.003.0055
fatcat:a3l77jef75afxffurxlwowe5uy
Articulatory feature based continuous speech recognition using probabilistic lexical modeling
2016
Computer Speech and Language
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., Articulatory feature based continuous speech recognition using probabilistic lexical modeling. Comput. Speech Lang. (2015), http://dx. ...
-Doss, M., Articulatory feature based continuous speech recognition using probabilistic lexical modeling. Comput. Speech Lang. (2015) , http://dx. ...
Acknowledgments This work was partly supported by the Swiss NSF through the grants "Flexible Grapheme-Based Automatic Speech Recognition (FlexASR, grant numbers 124985 and 146229)" and partly by the Commission ...
doi:10.1016/j.csl.2015.04.003
fatcat:6xv4ei5uuberdomxok4l6wsivy
Automatic Detection of Accent and Lexical Pronunciation Errors in Spontaneous Non-Native English Speech
2020
Interspeech 2020
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Three annotated corpora of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and ...
Detecting individual pronunciation errors and diagnosing pronunciation error tendencies in a language learner based on their speech are important components of computer-aided language learning (CALL). ...
Instead, an ASR system trained on non-native learners of English is used [36, 37] . ...
doi:10.21437/interspeech.2020-2881
dblp:conf/interspeech/KyriakopoulosKG20
fatcat:vfsdev64cza5bou2b2uv5mjhr4
Spoken word recognition in quiet and noise by native and non‐native listeners: Effects of age of immersion and vocabulary size
2009
Journal of the Acoustical Society of America
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to Mom and Dad, your desire for my success and happiness made me strive for both; to Kurt, Heidi, Christine, Michele, and Eric for your encouragement and love. to you all, I give my deepest expression of ...
Would recognition of the word be facilitated from its probabilistic phonotactics in ways that would not benefit a native listener because of the native listener's lexical focus? ...
The facilitative effects of probabilistic phonotactics for non-words occur because non-words fail to activate competing lexical representations. ...
doi:10.1121/1.4784690
fatcat:qhvgazsc7zb2nomhelp6tlhpru
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