“I’m a spawts guay”: Comparing the Use of Sociophonetic Variables in Speech and Twitter

This paper compares the rates of use of phonological variation across speech and, through variant spellings, Twitter data. Speech data came from New York sports personality Mike Francesa and one of his fans parodying him. The fan\u27s tweets, which include a large number of variant spellings, were also analyzed. Two patterns emerged. For the most salient variables, th-stopping and r deletion, the fan overshot Francesa\u27s use in speech and used them at that same higher rate in his tweets. However, for less-salient variables the fan used them at the same rate in speech as Francesa and used them at a far lower rate in their tweets. This suggests that the rate of use of a variable on Twitter can be used to investigate salience

Perception of Non-Phonological Reduction: A Case Study for Using Experimental Data to Investigate Rule-Based Phonology and Exemplar Theory

Flapping in American English is a very robust phonological process where underlying alveolar stops are instead produced as alveolar flaps intervocalically where the first vowel is stressed (DeJong, 1998). However, in fast speech, flapping occurs unpredictably in a much greater variety of phonological environments, at such high rates that "parsing reduction is the norm” (Warner & Tucker, 2010). It is no accident that non-phonological reduction is rampant in faster speech; Dalby (1986) argues that reduction of this sort is an articulatory strategy that speakers use to increase their speech rate. Flapping as a means of reducing articulatory effort and rate of production is a good one in American English. Not only are flaps non-contrastive in American English (meaning that the chance of incorrect parsing is much lower) but flaps also take approximately half as long to produce as a full stop (Zue, 1979). This presents a problem, however. Monolingual speaker of English are presented with non-phonological reduction with high frequency, and are furthermore not linguistically compelled to differentiate between alveolar stops and flaps since flaps are non-contrastive. Are they, then, capable of perceiving the difference between phonological and non-phonological reduction? Through three perceptual experiments, this study reveals that listeners are capable of perceiving these differences and, finally, that they react to the differences differently when the utterances are produced at different rates

Modeling the Perceptual Learning of Novel Dialect Features

Thesis (Ph.D.)--University of Washington, 2017-06All language use reflects the user's social identity in systematic ways. While humans can easily adapt to this sociolinguistic variation, automatic speech recognition (ASR) systems continue to struggle with it. This dissertation makes three main contributions. The first is to provide evidence that modern state-of-the-art commercial ASR systems continue to perform reliably worse on talkers from some social backgrounds. The second contribution is expanding our understanding of how and when human listeners who have been recently exposed to a new dialect rely more on social information about a talker than the acoustics. While human listeners' perceptions can be categorically shifted by giving them incorrect social information when listening to a new dialect, the same effect is much weaker when listening to their own dialect. The third contribution is computationally modeling listeners' bias towards their own dialect. Models trained using a dataset biased towards one dialect accurately reflected the behavior of listeners from that dialect. Further, explicitly including the dialect from which each training token was drawn during training and providing it at the time of classification improved classification accuracy with the second dialect while maintaining accuracy for the first. This can provide a behaviorally-accountable model for dialect adaptation in automatic speech recognition

Same data, different conclusions : radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed