On the measures of fluency in the assessment of spontaneous speech production by aphasic subjects

Fontaine, P.; Pillon, Agnesa; Courtray, Marie-Pierre de Partz de

doi:10.1080/02687039108248516

Cited by 27 publications

(11 citation statements)

References 72 publications

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“…and duration of pauses, complexity of syntax, and the integrity of the prosodic line (Deloche, Jean-Louis, & Seron, 1979;Feyereisen, Pillon, & Partz, 1991;Goodglass & Kaplan, 1983;Greenwald, Nadeau, & Rothi, 2000;Kerschensteiner, Poeck, & Brunner, 1972;Klein, Masur, Farber, Shinnar, & Rapin, 1992). Some authors recommend the use of total speaking time and the number of words produced to tell a story as additional measures of fluency (Kreindler, Mihailescu, & Fradis, 1980).…”

Section: Introductionmentioning

confidence: 95%

Speech Rate and Fluency in Children and Adolescents

Martins

Vieira

Loureiro

et al. 2007

Child Neuropsychology

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Reduced speech fluency is frequent in clinical paediatric populations, an unexplained finding. To investigate age related effects on speech fluency variables, we analysed samples of narrative speech (picture description) of 308 healthy children, aged 5 to 17 years, and studied its relation with verbal fluency tasks. All studied measures showed significant developmental effects. Speech rate and verbal fluency scores increased, while pauses, repetitions and locution time declined with age. Speech rate correlated with semantic fluency tasks suggesting that it also depends upon the efficacy of lexical retrieval. These results indicate that the interpretation of disorders of speech fluency in childhood must incorporate age appropriate norms.

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Section: Introductionmentioning

confidence: 95%

Speech Rate and Fluency in Children and Adolescents

Martins

Vieira

Loureiro

et al. 2007

Child Neuropsychology

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“…In second language testing (see, e.g., the speaking rubrics of the TOEFL test as reported on the ETS Web site [Educational Testing Service, 2004]) and second language research (e.g., Kormos & Dénes, 2004), as well as in the diagnosis of different language and speech disorders ( Feyereisen et al, 1991;Redmond, 2004;Shenker, 2006), fluency is an important factor to take into account. The script described and validated in this article may be useful to easily and objectively measure speech rate in terms of syllables per second without the need to transcribe speech beforehand.…”

Section: Speech Data For the Ifa Corpusmentioning

confidence: 99%

Praat script to detect syllable nuclei and measure speech rate automatically

Jong

Wempe

2009

Behavior Research Methods

447

281

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Covington et al., 2005). However, calculating speech rate by hand is a tedious task, which is therefore often not carried out. In this article, we present a script written in the software program Praat (Boersma & Weenink, 2007) that automatically detects syllable nuclei in order to calculate speech rate. The nucleus of a syllable, also called the peak, is the central part of a syllable (most commonly, the vowel in the syllable). Locating these syllable nuclei allows for a computation of number of syllables, which can be used to calculate speech rate.According to Tavakoli and Skehan (2005), fluency is multifaceted in nature. They distinguish three different facets of fluency: breakdown fluency (number and length of pauses), speed and density per time unit (speech rate), and repair fluency (false starts and repetitions). In second language testing practice, fluency is usually a score awarded by human judges who use several aspects of fluency in their judgment. However, Cucchiarini, Strik, and Boves (2002) have shown that of several objectively measured aspects of fluency, speech rate (as measured by phonemes per time unit) is the best predictor of subjective fluency. Kormos and Dénes (2004) likewise have shown that speech rate (in terms of number of syllables per time unit) is a good predictor of subjective fluency. We conclude that, for researchers wanting to include a measure of fluency, speech rate is an important factor to take into account. However, because of time constraints, this measure is often impossible to carry out. For instance, the script presented in this article was written in order to be able to measure the speech rate of 250 participants in a corpus of over 45 h of speech, a task that would take at least 8 months of full-time work for one person to measure by hand. In the context of a large-scale research project on the correlates of speaking proficiency carried out at the University of Amsterdam (What Is Speaking Proficiency: www.hum.uva.nl/wisp), we developed two tools to measure fluency automatically. For the purpose of measuring pauses in running speech, we wrote a script in the software program Praat to automatically detect silence in speech [a simplified version of which is now incorporated in the button To TextGrid (silences) in the Praat software]. For the purpose of estimating the speech rate of speech performances, we wrote a script in Praat that automatically detects syllable nuclei to compute speech rate in terms of syllables per time unit. In this article, we will present and validate the script for detecting syllable nuclei.For automatic speech recognition, speech rate is an important factor as well. Human listeners are able to understand both fast and slow speech. Speech recognizers implemented in computers, however, perform relatively poorly when In this article, we describe a method for automatically detecting syllable nuclei in order to measure speech rate without the need for a transcription. A script written in the software program Praat (Boersma & Weenink, 2007) detects syl...

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“…Such tests are generally composed of two types of subtests, namely semantic fluency and letter fluency tests, and are known to be differentially sensitive to different disease states because they require different cognitive processes (Feyereisen et al, 1991). Letter fluency tests are particularly sensitive to frontal lobe dysfunction (Miceli et al, 1981;Ramier and Hécaen, 1970).…”

Section: Test Materialsmentioning

confidence: 99%