Are Multilayer Perceptrons Adequate for Pattern Recognition and Verification?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Automatic ToBI prediction and alignment to speed manual labeling of prosody
Speech Communication - Special issue on speech annotation and corpus tools
Applying data mining techniques to corpus based prosodic modeling
Speech Communication
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
POST: using probabilities in language processing
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Analysis of inconsistencies in cross-lingual automatic ToBI tonal accent labeling
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Automatic Prosodic Event Detection Using Acoustic, Lexical, and Syntactic Evidence
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Glissando: a corpus for multidisciplinary prosodic studies in Spanish and Catalan
Language Resources and Evaluation
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In this paper we present an experimental study on how corpus-based automatic prosodic information labeling can be transferred from a source language to a different target language. The Spanish ESMA corpus is used to train models for the identification of the prominent words. Then, the models are used to identify the accented words of the English Boston University Radio News Corpus (BURNC). The inverse process (training the models with English data and testing with the Spanish corpus) is also contrasted with the results obtained in the conventional scenario: training and testing using the same corpus. We got up to 82.7% correct annotation rates in cross-lingual experiments, which contrast slightly with the accuracy obtained in a mono-lingual single speaker scenarios (86.6% for Spanish and 80.5% for English). Speaker independent monolingual recognition experiments have been also performed with the BURNC corpus, leading to cross-speakers results that go from 69.3% to 84.2% recognition rates. As these results are comparable to the ones obtained in the cross-lingual scenario we conclude that the new approach we defend has to face up with similar challenges as the ones presented in speaker independent scenarios.