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The basic idea behind this approach is the following: If listeners interpret a certain aspect of a sound segment as being due to an external factor, then they "subtract" this aspect from the sound segment when identifying its phoneme category.
(vi) MC-ELM-AE-TsEL-V: the 5-column MC-ELM-AE was conducted on SIF features for each sound segment to generate 5-column features, which were then fed to a TsEL algorithm.
(ii) SIF-ELM: the original SIF features of each sound segment were directly fed to the ELM classifier for classification, and then, only the second-stage ensemble learning was implemented to generate the final decision on all segments.
(iii) ELM-AE: the single-column ELM-AE was implemented on SIF features for each sound segment with the ELM classifier, and then, only the second-stage ensemble learning was used to get the final decision on all segments.
(iv) B-ELM-AE: ELM-AE was conducted twice on SIF features belonging to the same sound segment, respectively, and then, the bilinear model was implemented on these two ELM-AE features to generate bilinear features for classification with only the second-stage ELM-based ensemble learning on all segments.
(vii) MC-ELM-AE-TsEL-W: the 5-column MC-ELM-AE was conducted on SIF features for each sound segment to generate 5-column features, which were then fed to a TsEL algorithm with the weighted-voting-based ensemble learning algorithm in Section 2.4.
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They do this by making readers do two things: breaking syllables into sound segments and clustering these segments into bigger, abstract, flexible sound units.
Phonemes may be thought of as roughly equivalent to the sound segments known as consonants and vowels, and combinations of these segments make up syllables.
In speech, we call these noise-excitated sound segments "voiceless fricatives".
Forkheim et al. [39], investigated to detect only wheezes in isolated lung sound segments.
Phonetics is committed to consonants, syllables, words and sound segments, the human vocal tract and its parts (the tongue has five).
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