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            "abstractNote": "Purpose\n      The “bubble noise” technique has recently been introduced as a method to identify\n         the regions in time–frequency maps (i.e., spectrograms) of speech that are especially\n         important for listeners in speech recognition. This technique identifies regions of\n         “importance” that are specific to the speech stimulus and the listener, thus permitting\n         these regions to be compared across different listener groups. For example, in cross-linguistic\n         and second-language (L2) speech perception, this method identifies differences in\n         regions of importance in accomplishing decisions of phoneme category membership. This\n         research note describes the application of bubble noise to the study of language learning\n         for 3 different language pairs: Hindi English bilinguals' perception of the /v/–/w/\n         contrast in American English, native English speakers' perception of the tense/lax\n         contrast for Korean fricatives and affricates, and native English speakers' perception\n         of Mandarin lexical tone.\n      \n      \n      Conclusion\n      We demonstrate that this technique provides insight on what information in the speech\n         signal is important for native/first-language listeners compared to nonnative/L2 listeners.\n         Furthermore, the method can be used to examine whether L2 speech perception training\n         is effective in bringing the listener's attention to the important cues.",
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