113 research outputs found
Tonal placement in Tashlhiyt
In most languages, words contain vowels, elements of high intensity with rich harmonic structure, enabling the perceptual retrieval of pitch. By contrast, in Tashlhiyt, a Berber language, words can be composed entirely of voiceless segments. When an utterance consists of such words, the phonetic opportunity for the execution of intonational pitch movements is exceptionally limited. This book explores in a series of production and perception experiments how these typologically rare phonotactic patterns interact with intonational aspects of linguistic structure. It turns out that Tashlhiyt allows for a tremendously flexible placement of tonal events. Observed intonational structures can be conceived of as different solutions to a functional dilemma: The requirement to realise meaningful pitch movements in certain positions and the extent to which segments lend themselves to a clear manifestation of these pitch movements
How an intonation system accommodates to adverse phonological environments
In most languages, words contain vowels, elements of high intensity with rich
harmonic structure, enabling the perceptual retrieval of pitch. By contrast,
in Tashlhiyt, a Berber language, words can be composed entirely of voiceless
segments. When an utterance consists of such words, the phonetic opportunity
for the execution of intonational pitch movements is exceptionally limited.
This book explores in a series of production and perception experiments how
these typologically rare phonotactic patterns interact with intonational
aspects of linguistic structure. It turns out that Tashlhiyt allows for a
tremendously flexible placement of tonal events. Observed intonational
structures can be conceived of as different solutions to a functional dilemma:
The requirement to realise meaningful pitch movements in certain positions and
the extent to which segments lend themselves to a clear manifestation of these
pitch movements
Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses
Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions
Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses
Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions
Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses
Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions
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