12/5/2023 0 Comments Praat script duration measurement![]() A revised existing script and a new script for filled pauses are tested on accuracy. The current paper aims to automatically measure aspects of L2 fluency, including filled pauses, in both Dutch and English. Results revealed there was a significant difference between the proposed types of data and that%V and syllable rate best discriminated between them however, none of the above-mentioned parameters were significantly different between Persian-Azeri and Azeri-Persian data. fluency, however, is highly time-consuming because of the manual labour involved. These parameters are: %V (the proportion which speech is vocalic), ΔC (ln) (standard deviation of the natural-log normalized duration of consonantal intervals), ΔV (ln) (standard deviation of the natural-log normalized duration of vocalic intervals), nPVI- V (rate-normalized averaged durational differences between consecutive vocalic intervals) and syllable rate. A Praat script, DurationAnalyzer, was used to automatically calculate the acoustic correlates of durational parameters of speech rhythm. In order to control the effect of any unwanted variable, one minute (±5 seconds SD) of each sound file was extracted for further acoustic and statistical analyses. The recorded data were then annotated in five tiers: segment, CV-segment, CV-segment interval, CV-interval and syllable. This guide is not going to teach you how to write your own scripts, or even how to modify existing scripts in minor ways for your own needs. Persian-Azeri data is the type that is considered as the disguised data in this survey. Novem19:13 Using Praat Scripts Goal: Before you attempt to write your own Praat script, you will probably try to use one of the many existing scripts. Azeri speakers were also asked to narrate a lifetime experience once in Azeri (Azeri- Azeri data) and once in Persian (Azeri-Persian Data). Each Persian speaker was asked to narrate a lifetime experience once in Persian (Persian- Persian data), and once as an imitation of Azeri (Persian- Azeri data). All Persian speakers were monolingual and all Azeri speakers were bilingual speakers of Azeri and Persian, who spoke Azeri as their mother tongue and Persian as their second language. To do so, continuous speech of 5 speakers of Standard Persian and 5 speakers of Azeri, Tabrizi variety, were chosen for the acoustic and statistical analysis after completing the validity procedures. Using the auditory-acoustic approach, the present study examines the possibility of using durational acoustic parameters of speech rhythm for detecting the Persian speakers’ use of Azeri as a form of voice disguise.
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