Literature DB >> 16287653

Towards an understanding of speech and song perception.

Rachel M van Besouw1, David M Howard, Sten Ternström.   

Abstract

The human singing voice plays an important role in music of all societies. It is an extremely flexible instrument and is capable of producing a tremendous range of sounds. As such, the human voice can be hard to classify and poses a major challenge for automatic audio discrimination and classification systems. Speech/song discrimination is an implicit goal of speech/music discrimination, where a division is sought between speech and song, such that the singing voice can be grouped together with other musical instruments in the same category. However, the division between speech and song is unclear and even human attempts at speech/song discrimination can be highly subjective and open to discussion. In this paper we present the results of a test that was designed to investigate differences in auditory perception for speech and song. Twenty-four subjects were instructed to attend to either the words or pitch, or both words and pitch of context-free spoken and sung phrases. After presentation of each phrase, subjects were asked to either type the words that they recalled, or select the correct pitch contour from a choice of four graphical representations, or do both, depending on the task specified before presentation of the phrase. The results of the experiment show a decrease in the amount of linguistic information retained by subjects for sung phrases and also a decrease in accuracy of response for the sung phrases when subjects attended to both words and pitch instead of words or pitch alone.

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Year:  2005        PMID: 16287653     DOI: 10.1080/14015430500262160

Source DB:  PubMed          Journal:  Logoped Phoniatr Vocol        ISSN: 1401-5439            Impact factor:   1.487


  2 in total

1.  Words and melody are intertwined in perception of sung words: EEG and behavioral evidence.

Authors:  Reyna L Gordon; Daniele Schön; Cyrille Magne; Corine Astésano; Mireille Besson
Journal:  PLoS One       Date:  2010-03-31       Impact factor: 3.240

2.  Using machine learning analysis to interpret the relationship between music emotion and lyric features.

Authors:  Liang Xu; Zaoyi Sun; Xin Wen; Zhengxi Huang; Chi-Ju Chao; Liuchang Xu
Journal:  PeerJ Comput Sci       Date:  2021-11-15
  2 in total

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