Prosodic modules for speech recognition and understanding in VERBMOBIL

Within VERBMOBIL, a large project on spoken language research in Germany, two modules for detecting and recognizing prosodic events have been developed. One module operates on speech signal parameters and the word hypothesis graph, whereas the other module, designed for a novel, highly interactive architecture, only uses speech signal parameters as its input. Phrase boundaries, sentence modality, and accents are detected. The recognition rates in spontaneous dialogs are for accents up to 82,5%, for phrase boundaries up to 91,7%

Porting the galaxy system to Mandarin Chinese

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 83-86).by Chao Wang.M.S

Neural correlates of emotion word processing: the complex relation between emotional valence and arousal

Poster Session 1: no. 2The Conference's website is located at http://events.unitn.it/en/psb2010Emotion is characterised by a two-dimensional structure: valence describes the extent to which an emotion is positive or negative, whereas arousal refers to the intensity of an emotion, how exciting or calming it is. Emotional content of verbal material influences cognitive processing during lexical decision, naming, emotional Stroop task and many others. Converging findings showed that emotionally valenced words (positive or negative) are processed faster than neutral words, as shown by reaction time and ERP measures, suggesting a prioritisation of emotional …published_or_final_versio

Robust methods for Chinese spoken document retrieval.

Hui Pui Yu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 158-169).Abstracts in English and Chinese.Abstract --- p.2Acknowledgements --- p.6Chapter 1 --- Introduction --- p.23Chapter 1.1 --- Spoken Document Retrieval --- p.24Chapter 1.2 --- The Chinese Language and Chinese Spoken Documents --- p.28Chapter 1.3 --- Motivation --- p.33Chapter 1.3.1 --- Assisting the User in Query Formation --- p.34Chapter 1.4 --- Goals --- p.34Chapter 1.5 --- Thesis Organization --- p.35Chapter 2 --- Multimedia Repository --- p.37Chapter 2.1 --- The Cantonese Corpus --- p.37Chapter 2.1.1 --- The RealMedia´ёØCollection --- p.39Chapter 2.1.2 --- The MPEG-1 Collection --- p.40Chapter 2.2 --- The Multimedia Markup Language --- p.42Chapter 2.3 --- Chapter Summary --- p.44Chapter 3 --- Monolingual Retrieval Task --- p.45Chapter 3.1 --- Properties of Cantonese Video Archive --- p.45Chapter 3.2 --- Automatic Speech Transcription --- p.46Chapter 3.2.1 --- Transcription of Cantonese Spoken Documents --- p.47Chapter 3.2.2 --- Indexing Units --- p.48Chapter 3.3 --- Known-Item Retrieval Task --- p.49Chapter 3.3.1 --- Evaluation ´ؤ Average Inverse Rank --- p.50Chapter 3.4 --- Retrieval Model --- p.51Chapter 3.5 --- Experimental Results --- p.52Chapter 3.6 --- Chapter Summary --- p.53Chapter 4 --- The Use of Audio and Video Information for Monolingual Spoken Document Retrieval --- p.55Chapter 4.1 --- Video-based Segmentation --- p.56Chapter 4.1.1 --- Metric Computation --- p.57Chapter 4.1.2 --- Shot Boundary Detection --- p.58Chapter 4.1.3 --- Shot Transition Detection --- p.67Chapter 4.2 --- Audio-based Segmentation --- p.69Chapter 4.2.1 --- Gaussian Mixture Models --- p.69Chapter 4.2.2 --- Transition Detection --- p.70Chapter 4.3 --- Performance Evaluation --- p.72Chapter 4.3.1 --- Automatic Story Segmentation --- p.72Chapter 4.3.2 --- Video-based Segmentation Algorithm --- p.73Chapter 4.3.3 --- Audio-based Segmentation Algorithm --- p.74Chapter 4.4 --- Fusion of Video- and Audio-based Segmentation --- p.75Chapter 4.5 --- Retrieval Performance --- p.76Chapter 4.6 --- Chapter Summary --- p.78Chapter 5 --- Document Expansion for Monolingual Spoken Document Retrieval --- p.79Chapter 5.1 --- Document Expansion using Selected Field Speech Segments --- p.81Chapter 5.1.1 --- Annotations from MmML --- p.81Chapter 5.1.2 --- Selection of Cantonese Field Speech --- p.83Chapter 5.1.3 --- Re-weighting Different Retrieval Units --- p.84Chapter 5.1.4 --- Retrieval Performance with Document Expansion using Selected Field Speech --- p.84Chapter 5.2 --- Document Expansion using N-best Recognition Hypotheses --- p.87Chapter 5.2.1 --- Re-weighting Different Retrieval Units --- p.90Chapter 5.2.2 --- Retrieval Performance with Document Expansion using TV-best Recognition Hypotheses --- p.90Chapter 5.3 --- Document Expansion using Selected Field Speech and N-best Recognition Hypotheses --- p.92Chapter 5.3.1 --- Re-weighting Different Retrieval Units --- p.92Chapter 5.3.2 --- Retrieval Performance with Different Indexed Units --- p.93Chapter 5.4 --- Chapter Summary --- p.94Chapter 6 --- Query Expansion for Cross-language Spoken Document Retrieval --- p.97Chapter 6.1 --- The TDT-2 Corpus --- p.99Chapter 6.1.1 --- English Textual Queries --- p.100Chapter 6.1.2 --- Mandarin Spoken Documents --- p.101Chapter 6.2 --- Query Processing --- p.101Chapter 6.2.1 --- Query Weighting --- p.101Chapter 6.2.2 --- Bigram Formation --- p.102Chapter 6.3 --- Cross-language Retrieval Task --- p.103Chapter 6.3.1 --- Indexing Units --- p.104Chapter 6.3.2 --- Retrieval Model --- p.104Chapter 6.3.3 --- Performance Measure --- p.105Chapter 6.4 --- Relevance Feedback --- p.106Chapter 6.4.1 --- Pseudo-Relevance Feedback --- p.107Chapter 6.5 --- Retrieval Performance --- p.107Chapter 6.6 --- Chapter Summary --- p.109Chapter 7 --- Conclusions and Future Work --- p.111Chapter 7.1 --- Future Work --- p.114Chapter A --- XML Schema for Multimedia Markup Language --- p.117Chapter B --- Example of Multimedia Markup Language --- p.128Chapter C --- Significance Tests --- p.135Chapter C.1 --- Selection of Cantonese Field Speech Segments --- p.135Chapter C.2 --- Fusion of Video- and Audio-based Segmentation --- p.137Chapter C.3 --- Document Expansion with Reporter Speech --- p.137Chapter C.4 --- Document Expansion with N-best Recognition Hypotheses --- p.140Chapter C.5 --- Document Expansion with Reporter Speech and N-best Recognition Hypotheses --- p.140Chapter C.6 --- Query Expansion with Pseudo Relevance Feedback --- p.142Chapter D --- Topic Descriptions of TDT-2 Corpus --- p.145Chapter E --- Speech Recognition Output from Dragon in CLSDR Task --- p.148Chapter F --- Parameters Estimation --- p.152Chapter F.1 --- "Estimating the Number of Relevant Documents, Nr" --- p.152Chapter F.2 --- "Estimating the Number of Terms Added from Relevant Docu- ments, Nrt , to Original Query" --- p.153Chapter F.3 --- "Estimating the Number of Non-relevant Documents, Nn , from the Bottom-scoring Retrieval List" --- p.153Chapter F.4 --- "Estimating the Number of Terms, Selected from Non-relevant Documents (Nnt), to be Removed from Original Query" --- p.154Chapter G --- Abbreviations --- p.155Bibliography --- p.15

The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study

Carminati MN, Knoeferle P. The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study. Presented at the Architectures and Mechanisms of Language and Processing (AMLaP), Riva del Garda, Italy

Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning

This study addresses whether there is anything special about learning a third language, as compared to learning a second language, that results solely from the order of acquisition. We use a computational model based on the mathematical framework of Linear Discriminative Learning to explore this question for the acquisition of a small trilingual vocabulary, with English as L1, German or Mandarin as L2, and Mandarin or Dutch as L3. Our simulations reveal that when qualitative differences emerge between the learning of a first, second and third language, these differences emerge from distributional properties of the particular languages involved rather than the order of acquisition per se, or any difference in learning mechanism. One such property is the number of homophones in each language, since within-language homophones give rise to errors in production. Our simulations also show the importance of suprasegmental information in determining the kinds of production errors made

English prosodic marking of Information Structure by L1-Japanese second language learners

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018