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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 Hits 1 – 8 of 8 1 Dealing with linguistic mismatches for automatic speech recognition Yang, Xuesong. - 2019 BASE Show details 2 Semi-supervised learning for acoustic and prosodic modeling in speech applications Huang, Jui Ting. - 2012 BASE Show details 3 Beiträge zur statistischen Modellierung und effizienten Dekodierung in der automatischen Spracherkennung ; Contributions to statistical modeling and effecient decoding in automatic speech recognition Willett, Daniel. - 2006 BASE Show details 4 Automatic Recognition of Cantonese-English Code-Mixing Speech Joyce Y. C. Chan; Houwei Cao; P. C. Ching... In: http://wing.comp.nus.edu.sg/~antho/O/O09/O09-5003.pdf BASE Show details 5 COMBINING SPEECH RECOGNITION AND ACOUSTIC WORD EMOTION MODELS FOR ROBUST TEXT-INDEPENDENT EMOTION RECOGNITION Björn Schuller; Bogdan Vlasenko; Dejan Arsic... In: http://www.mmk.ei.tum.de/publ/pdf/08/08sch9.pdf BASE Show details 6 Towards a non-parametric acoustic model: An acoustic decision tree for observation probability calculation,” Interspeech 2008 Michael L. Seltzer; Alex Acero In: http://www.cs.cmu.edu/~ychiu/ychiu_web_files/nonparametric.pdf Abstract: Modern automatic speech recognition systems use Gaussian mixture models (GMM) on acoustic observations to model the probability of producing a given observation under any one of many hidden discrete phonetic states. This paper investigates the feasibility of using an acoustic decision tree to directly model these probabilities. Unlike the more common phonetic decision tree, which asks questions about phonetic context, an acoustic decision tree asks questions about the vector-valued observations. Three different types of acoustic questions are proposed and evaluated, including LDA, PCA, and MMI questions. Frame classification experiments are run on a subset of the Switchboard corpus. On these experiments, the acoustic decision tree produces slightly better results than maximum likelihood trained GMMs, with significantly less computation. Some theoretical advantages of the acoustic decision tree are discussed, including more economical use of the training data and reduced mismatch between the acoustic model and the true probability distribution of the phonetic labels. Keyword: Acoustic Modeling; Decision Trees; Index Terms—Speech Recognition URL: http://www.cs.cmu.edu/~ychiu/ychiu_web_files/nonparametric.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.6754 BASE Hide details 7 INTEGRATION OF MULTIPLE FEATURE SETS FOR REDUCING AMBIGUITY IN ASR Richard Rose In: http://www.ece.mcgill.ca/~rrose1/papers/rose_parya_icassp07.pdf BASE Show details 8 Towards a Non-Parametric Acoustic Model: An Acoustic Decision Tree for Observation Probability Calculation Michael L. Seltzer; Alex Acero; Yu-hsiang Bosco Chiu In: http://research.microsoft.com/pubs/78716/ADT.pdf BASE Show details

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