Rhythmic structure of Hindi and English: new insights from a computational analysis

Das, Tanusree; Singh, Latika; Singh, Nandini Chatterjee

doi:10.1016/s0079-6123(07)68017-0

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…The authors tested this in English, Spanish, Slovak, and Czech: four languages that vary in rhythmic properties to a comparable extent with the present range of Indo-European languages with phonetic (primarily Roman) scripts. Whilst there have been demonstrations of language-specific differences in speech rhythm (Dauer, 1983; Grabe and Low, 2002; Das et al, 2008) and there is an ongoing search for metrics to best capture them (Patel et al, 2006; Nolan and Asu, 2009; Turk and Shattuck-Hufnagel, 2013; Dellwo et al, 2015), we would argue that the presence of a roughly regular beat of some kind is inherent to them all. Consistent with our cross-lingual finding for rhythmic regularity processing, Goswami and coworkers have demonstrated a universal role for aspects of sound rise-time as a fundamental, language-general feature.…”

Section: Discussionmentioning

confidence: 99%

The Beat to Read: A Cross-Lingual Link between Rhythmic Regularity Perception and Reading Skill

Bekius

Cope

Grube

2016

Front. Hum. Neurosci.

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This work assesses one specific aspect of the relationship between auditory rhythm cognition and language skill: regularity perception. In a group of 26 adult participants, native speakers of 11 different native languages, we demonstrate a strong and significant correlation between the ability to detect a “roughly” regular beat and rapid automatized naming (RAN) as a measure of language skill (Spearman's rho, −0.47, p < 0.01). There was no such robust relationship for the “mirror image” task of irregularity detection, i.e., the ability to detect ongoing small deviations from a regular beat. The correlation between RAN and regularity detection remained significant after partialling out performance on the irregularity detection task (rho, −0.41, p, 0.022), non-verbal IQ (rho, −0.37, p < 0.05), or musical expertise (rho, −0.31, p < 0.05). Whilst being consistent with the “shared resources model” in terms of rhythm as a common basis of language and music, evolutionarily as well as in individual development, the results also document how two related rhythm processing abilities relate differently to language skill. Specifically, the results support a universal relationship between rhythmic regularity detection and reading skill that is robust to accounting for differences in fluid intelligence and musical expertise, and transcends language-specific differences in speech rhythm.

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Section: Discussionmentioning

confidence: 99%

The Beat to Read: A Cross-Lingual Link between Rhythmic Regularity Perception and Reading Skill

Bekius

Cope

Grube

2016

Front. Hum. Neurosci.

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“…A review of the imaging literature, suggests a role for right STG during rhythmic implementation of syllables (Riecker, Wildgruber, Dogil, Grodd, & Ackermann, 2002). Since Hindi has been classified as a syllable-timed language (Bhatia, 1996;Das, Singh, & Singh, 2008) we attribute the activation of STG in the right hemisphere to the syllable timed rhythm arising from the covert articulatory movements of Hindi. In contrast, relative to reading in Hindi, no orthography specific activation was seen for reading in English.…”

Section: Effects Of Fluencymentioning

confidence: 90%

Reading different orthographies: an fMRI study of phrase reading in Hindi–English bilinguals

et al. 2009

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The aim of the present study was to use functional imaging to compare cortical activations involved in reading Hindi and English that differ markedly in terms of their orthographies by a group of late bilinguals, more fluent in Hindi (L1) than English (L2). English is alphabetic and linear, in that vowels and consonants are arranged sequentially. In contrast, Hindi, written in Devanagari, is an alphasyllabary and non-linear writing system wherein vowels are placed around consonants making it a visually complex script. Additionally, the grapheme to phoneme mapping in English is opaque while Devanagari is transparent. Effects of reading fluency were seen in significantly slower reading times and direct EnglishHindi comparison showed left putamen activation for the less fluent language (English). Direct Hindi-English orthography comparisons revealed activation in the temporal pole and caudate nucleus of the right hemisphere, cortical areas known to be involved in semantic and visual processing. We also find activation in right superior temporal gyrus, which we attribute to the syllabic rhythm of Hindi. Our

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“…Previous studies have used extensive speech corpora to show that the production frequencies of linguistic units and, hence, the periodicity of the speech signal could be mapped as peaks in the acoustic amplitude envelope power spectra (Das et al, 2008;Tilsen and Johnson 2008;Chandrasekaran et al, 2009;Tilsen and Arvaniti, 2013). However, even for larger data sets, 1/f trend removal has been deemed necessary for saliently distinguishing spectral peaks related to speech rhythm (Chandrasekaran et al, 2009; see also Ruspantini et al, 2012).…”

Section: Figmentioning

confidence: 99%

“…Frequency-domain signal processing tools are being increasingly employed to investigate the acoustic (Das et al, 2008;Tilsen and Johnson, 2008;Tilsen and Arvaniti, 2013) and muscular (electromyographic, EMG) (e.g., Ruspantini et al, 2012) aspects of speech signals and speech rhythm. The acoustic envelope carries temporal features which reflect rhythm in speech (Rosen, 1992).…”

Section: Introductionmentioning

confidence: 99%

A multimodal spectral approach to characterize rhythm in natural speech

Alexandrou

Saarinen

Kujala

et al. 2016

The Journal of the Acoustical Society of America

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Human utterances demonstrate temporal patterning, also referred to as rhythm. While simple oromotor behaviors (e.g., chewing) feature a salient periodical structure, conversational speech displays a time-varying quasi-rhythmic pattern. Quantification of periodicity in speech is challenging. Unimodal spectral approaches have highlighted rhythmic aspects of speech. However, speech is a complex multimodal phenomenon that arises from the interplay of articulatory, respiratory, and vocal systems. The present study addressed the question of whether a multimodal spectral approach, in the form of coherence analysis between electromyographic (EMG) and acoustic signals, would allow one to characterize rhythm in natural speech more efficiently than a unimodal analysis. The main experimental task consisted of speech production at three speaking rates; a simple oromotor task served as control. The EMG-acoustic coherence emerged as a sensitive means of tracking speech rhythm, whereas spectral analysis of either EMG or acoustic amplitude envelope alone was less informative. Coherence metrics seem to distinguish and highlight rhythmic structure in natural speech.

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Rhythmic structure of Hindi and English: new insights from a computational analysis

Cited by 9 publications

References 9 publications

The Beat to Read: A Cross-Lingual Link between Rhythmic Regularity Perception and Reading Skill

The Beat to Read: A Cross-Lingual Link between Rhythmic Regularity Perception and Reading Skill

Reading different orthographies: an fMRI study of phrase reading in Hindi–English bilinguals

A multimodal spectral approach to characterize rhythm in natural speech

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