Also, connectionist networks have been used to automatically syllabify random strings of segments in Berber. An exciting development is computational algorithms that can directly calculate the relative sonority of acoustic samples and potentially segment them, based on various phonetic parameters these algorithms have contributed to automated speech recognition. However, different studies counter that this knowledge can be acquired by extrapolating statistical generalizations from the lexicons of those languages, without a prior bias concerning preferred sonority differentials. For example, experiments asking speakers of various languages to rate the naturalness of or pronounce forms containing non-native clusters show that universal markedness constraints involving sonority predict accuracy on such tasks. Recent research on sonority has revived a debate about its innateness. However, while generalizations of this kind are strong, some have counterexamples, raising questions about the adequacy of sonority and how to encode it grammatically. These observations have led to implicatures such as lower sonority nuclei entailing the existence of nuclei from all higher sonority classes in a particular language. Furthermore, the propensity for a segment to pattern as moraic is proportional to its sonority. Thus onsets prototypically contain an obstruent plus an approximant. A primary function of sonority is to linearize segments within syllables: more sonorous sounds tend to occur more closely to the peak. The phonetic basis of sonority is contentious it is roughly but imperfectly correlated with loudness. Many versions of the sonority hierarchy exist a common one is vowels > glides > liquids > nasals > obstruents. Sonority is a nonbinary phonological feature categorizing sounds into a relative scale.
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