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Mismatched crowdsourcing is a technique to derive speech transcriptions using crowd-workers unfamiliar with the language being spoken. This technique is especially useful for under-resourced languages since it is hard to hire native transcribers. In this paper, we demonstrate that using mismatched transcription for adaptation improves performance of speech recognition under limited matched training...
This paper presents a novel method for acoustic modeling of an under-resourced language by “mapping” from acoustic models of well-resourced languages. The proposed method can be considered as a “many-to-one mapping” method where one speech unit in the target language is built as a linear combination of the source speech unit models and hence we can explicitly observe the relationship of the source...
We propose strategies for a state-of-the-art keyword search (KWS) system developed by the SINGA team in the context of the 2014 NIST Open Keyword Search Evaluation (OpenKWS14) using conversational Tamil provided by the IARPA Babel program. To tackle low-resource challenges and the rich morphological nature of Tamil, we present highlights of our current KWS system, including: (1) Submodular optimization...
This paper considers an unsupervised data selection problem for the training data of an acoustic model and the vocabulary coverage of a keyword search system in low-resource settings. We propose to use Gaussian component index based n-grams as acoustic features in a submodular function for unsupervised data selection. The submodular function provides a near-optimal solution in terms of the objective...
The mineralogy and environmental history of Mars are been extensively investigated through remote sensing observations paired with laboratory and in situ experiments. A significant portion of these experiments is being devoted to the identification and quantification of different iron oxides present in the Martian terrains. Although such experiments can provide valuable information regarding the presence...
Model M, a novel class-based exponential language model, has been shown to significantly outperform word n-gram models in state-of-the-art machine translation and speech recognition systems. The model was motivated by the observation that shrinking the sum of the parameter magnitudes in an exponential language model leads to better performance on unseen data. Being a class-based language model, Model...
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