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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 27 1 Domain-Adversarial Based Model with Phonological Knowledge for Cross-Lingual Speech Recognition Qingran Zhan; Xiang Xie; Chenguang Hu; Juan Zuluaga-Gomez; Jing Wang; Haobo Cheng In: Electronics; Volume 10; Issue 24; Pages: 3172 (2021) BASE Show details 2 MISPRONUNCIATION DETECTION AND DIAGNOSIS IN MANDARIN ACCENTED ENGLISH SPEECH Khanal, Subash In: Theses and Dissertations--Electrical and Computer Engineering (2020) BASE Show details 3 Articulation modelling of vowels in dysarthric and non-dysarthric speech Albalkhi, Rahaf. - 2020 BASE Show details 4 The Vowel System of Korebaju Vega Rodríguez, Jenifer In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02420035 ; INTERSPEECH 2019, Sep 2019, Graz, Austria. ⟨10.21437/Interspeech.2019-3210⟩ ; https://www.interspeech2019.org/ (2019) BASE Show details 5 The effects of L1 AP-initial boundary tones and laryngeal features in Korean adaptation of Japanese plosives followed by a H or L vowel Kim, Hyunsoon In: Glossa: a journal of general linguistics; Vol 4, No 1 (2019); 49 ; 2397-1835 (2019) BASE Show details 6 Articulatory representations to address acoustic variability in speech ... Sivaraman, Ganesh. - : Digital Repository at the University of Maryland, 2017 BASE Show details 7 Articulatory representations to address acoustic variability in speech Sivaraman, Ganesh. - 2017 Abstract: The past decade has seen phenomenal improvement in the performance of Automatic Speech Recognition (ASR) systems. In spite of this vast improvement in performance, the state-of-the-art still lags significantly behind human speech recognition. Even though certain systems claim super-human performance, this performance often is sub-par across domains and across datasets. This gap is predominantly due to the lack of robustness against speech variability. Even clean speech is extremely variable due to a large number of factors such as voice characteristics, speaking style, speaking rate, accents, casualness, emotions and more. The goal of this thesis is to investigate the variability of speech from the perspective of speech production, put forth robust articulatory features to address this variability, and to incorporate these features in state-of-the-art ASR systems in the best way possible. ASR systems model speech as a sequence of distinctive phone units like beads on a string. Although phonemes are distinctive units in the cognitive domain, their physical realizations are extremely varied due to coarticulation and lenition which are commonly observed in conversational speech. The traditional approaches deal with this issue by performing di-, tri- or quin-phone based acoustic modeling but are insufficient to model longer contextual dependencies. Articulatory phonology analyzes speech as a constellation of coordinated articulatory gestures performed by the articulators in the vocal tract (lips, tongue tip, tongue body, jaw, glottis and velum). In this framework, acoustic variability is explained by the temporal overlap of gestures and their reduction in space. In order to analyze speech in terms of articulatory gestures, the gestures need to be estimated from the speech signal. The first part of the thesis focuses on a speaker independent acoustic-to-articulatory inversion system that was developed to estimate vocal tract constriction variables (TVs) from speech. The mapping from acoustics to TVs was learned from the multi-speaker X-ray Microbeam (XRMB) articulatory dataset. Constriction regions from TV trajectories were defined as articulatory gestures using articulatory kinematics. The speech inversion system combined with the TV kinematics based gesture annotation provided a system to estimate articulatory gestures from speech. The second part of this thesis deals with the analysis of the articulatory trajectories under different types of variability such as multiple speakers, speaking rate, and accents. It was observed that speaker variation degraded the performance of the speech inversion system. A Vocal Tract Length Normalization (VTLN) based speaker normalization technique was therefore developed to address the speaker variability in the acoustic and articulatory domains. The performance of speech inversion systems was analyzed on an articulatory dataset containing speaking rate variations to assess if the model was able to reliably predict the TVs in challenging coarticulatory scenarios. The performance of the speech inversion system was analyzed in cross accent and cross language scenarios through experiments on a Dutch and British English articulatory dataset. These experiments provide a quantitative measure of the robustness of the speech inversion systems to different speech variability. The final part of this thesis deals with the incorporation of articulatory features in state-of-the-art medium vocabulary ASR systems. A hybrid convolutional neural network (CNN) architecture was developed to fuse the acoustic and articulatory feature streams in an ASR system. ASR experiments were performed on the Wall Street Journal (WSJ) corpus. Several articulatory feature combinations were explored to determine the best feature combination. Cross-corpus evaluations were carried out to evaluate the WSJ trained ASR system on the TIMIT and another dataset containing speaking rate variability. Results showed that combining articulatory features with acoustic features through the hybrid CNN improved the performance of the ASR system in matched and mismatched evaluation conditions. The findings based on this dissertation indicate that articulatory representations extracted from acoustics can be used to address acoustic variability in speech observed due to speakers, accents, and speaking rates and further be used to improve the performance of Automatic Speech Recognition systems. Keyword: Articulatory features; Articulatory phonology; Automatic Speech Recognition; Electrical engineering; Linguistics; Speaker adaptation; Speech inversion; Speech variability URL: https://doi.org/10.13016/M2BK16R29 http://hdl.handle.net/1903/20422 BASE Hide details 8 Stress Effects on Stop Bursts in Five Languages Tabain, Marija; Breen, Gavan; Butcher, Andrew... In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 7, No 1 (2016); 16 ; 1868-6354 (2016) BASE Show details 9 Stress Effects on Stop Bursts in Five Languages Tabain, M; Breen, G; Butcher, Andrew Richard. - : Ubiquity Press, 2016 BASE Show details 10 Exceptional nasal-stop inventories Piñeros, Carlos-Eduardo. - 2016 BASE Show details 11 Exceptional nasal-stop inventories ; Inventaris excepcionals d’oclusives nasals Piñeros, Carlos-Eduardo In: Catalan Journal of Linguistics; Vol. 15 (2016): Les excepcions en fonologia; p. 67-100 (2016) BASE Show details 12 Place oppositions in English coronal obstruents: an ultrasound study Gonzalez Ochoa, Simon Dario. - 2015 BASE Show details 13 Palate-referenced Articulatory Features for Acoustic-to-Articulator Inversion Ji, An; Johnson, Michael T; Berry, Jeffrey J. In: Speech Pathology and Audiology Faculty Research and Publications (2014) BASE Show details 14 An Emergent Approach to the Guttural Natural Class Sylak-Glassman, John In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2013 Annual Meeting on Phonology ; 2377-3324 (2014) BASE Show details 15 Multiview feature learning for speech recognition Bharadwaj, Sujeeth. - 2011 BASE Show details 16 Speech Bandwidth Extension Using Articulatory Features Shin, Dongeek. - 2011 BASE Show details 17 Cross-lingual automatic speech recognition using tandem features Lal, Partha. - : The University of Edinburgh, 2011 BASE Show details 18 Perception of initial obstruent voicing is influenced by gestural organization Best, Catherine,; Hallé, Pierre In: ISSN: 0095-4470 ; EISSN: 1095-8576 ; Journal of Phonetics ; https://halshs.archives-ouvertes.fr/halshs-00683110 ; Journal of Phonetics, Elsevier, 2010, 38, pp.109-126 (2010) BASE Show details 19 Statistical Mapping between Articulatory Movements and Acoustic Spectrum Using a Gaussian Mixture Model Tomoki Toda In: http://spalab.naist.jp/~tomoki/Tomoki/Journals/SPECOM-Mar-2008_ArtSpMap.pdf (2007) BASE Show details 20 Multilingual Articulatory Features for Speech Recognition Ore, Brian M. In: http://rave.ohiolink.edu/etdc/view?acc_num=wright1176169264 (2007) BASE Show details Page: 1 2

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