Deep Bottleneck Features for Spoken Language Identification
نویسندگان
چکیده
منابع مشابه
Deep Bottleneck Features for Spoken Language Identification
A key problem in spoken language identification (LID) is to design effective representations which are specific to language information. For example, in recent years, representations based on both phonotactic and acoustic features have proven their effectiveness for LID. Although advances in machine learning have led to significant improvements, LID performance is still lacking, especially for ...
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Recently, deep bottleneck features (DBF) extracted from a deep neural network (DNN) containing a narrow bottleneck layer, have been applied for language identification (LID), and yield significant performance improvement over state-of-the-art methods on NIST LRE 2009. However, the DNN is trained using a large corpus of specific language which is not directly related to the LID task. More recent...
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Empirical results have shown that many spoken language identification systems based on hand-coded features perform poorly on small speech samples where a human would be successful. A hypothesis for this low performance is that the set of extracted features is insufficient. A deep architecture that learns features automatically is implemented and evaluated on several datasets.
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ژورنال
عنوان ژورنال: PLoS ONE
سال: 2014
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0100795