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Articulatory features are used as an universal set of speech attributes shared across many different languages. Some multilingual and cross-language speech recognition systems using articulatory features have been shown to improve the performance. The existing articulatory features are defined by phonetician as a set of articulatory descriptions of phones, which represent some semantic information...
In a series of studies, articulatory features used as speech attributes for automatic speech recognition systems have been shown to improve the performance. The existing articulatory features are defined by phonetician as a set of articulatory descriptions of phones, which represent some semantic information explaining how humans produce speech sounds via the interaction of different physiological...
There has been a challenging research topic on exploring an universal set of speech attributes sharing among a large number of languages for detection-based bottom-up cross-language speech recognition. In some recent research works, articulatory features are used as an universal set of speech attributes shared across many different languages. Since they are defined by human as a set of semantic articulatory...
Cross-language transfer speech recognition aims to transform phoneme models for a source language to recognize a target language lacking labeled data and other linguistic resources. In this paper, sparse auto-encoder, a deep learning method, is introduced to derive shared speech features between source and target language using semi-supervised learning. It can extract the shared representation of...
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