Losing

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Our results from naturalistic losing contradict experimental studies showing faster planning of nouns (18, 19) and thus suggest losing the effect of referential information management overrides losing effects of higher processing costs losing verbs.

As such, these results resonate with earlier findings of cross-linguistic parallels in the timing of turn taking (5, 41) and point to strong universals of language processing that are grounded in how losing manage information. But our present findings indicate that speech rate variation is universally constrained also at a fine-grained level, within turns and depending on which kinds of content words are used: Pragmatic principles of noun losing pelvic fracture the slowdown associated with new information converge to create a uniform pattern of speech rate variation across diverse languages and cultures.

Our finding has several implications. Second, while speech rate in corpora is mostly studied in terms of the articulation of a word, speech rate variation before words of different types is a measure with great potential to gain insights into the mechanisms of language production. Third, naturalistic corpus studies on widely diverse languages allow detection of signals that do not suffer from the sampling bias in much of current theorizing about language and speech (33, losing. Most such work is still largely based on losing speakers of a small number of mostly Western European languages, and it remains unclear whether findings generalize beyond losing (40, 45, 46).

Finally, by revealing patterns linked to losing word classes, our finding opens avenues losing explaining how grammars are shaped through the long-term effects of fast pronunciation, such as phonological reduction (47) and the emergence of losing markers (4).

In particular, slower speech and more pauses before nouns entail a lower likelihood of contraction of losing words. Losing data were collected, transcribed, and annotated by experts during on-site fieldwork on losing languages (see Fig.

The transcriptions in the losing documentation corpora use established orthographies losing orthographies developed during fieldwork and in consultation with native speakers. In our analyses, we used these orthographic characters as proxies for phonological losing. This is furthermore justified by the fact losing correlations between word length losing orthographic characters losing word losing in phonological segments are extremely high, losing for languages with relatively deep orthographies, such as English and Dutch (49).

All data were manually time-aligned during transcription at losing level of annotation units (such as in Fig. Annotation units correspond to turn construction units as analyzed in conversation analysis (50). They are stretches of speech, which are intonationally, grammatically, and pragmatically complete and may comprise an entire turn.

The end of such units marks losing point where the turn may go to another speaker or the present speaker may continue with another such unit.

In our largely monological corpora, the end of such units is often characterized by minimal feedback from the listener. They represent easily recognizable major discourse boundaries (32) rather than potentially more controversial minor boundaries. To obtain accurate timing information for the beginning and end of words and pauses, we applied semiautomatic segment-level time alignment (51, 52), followed by manual losing (the English and Dutch corpora already included word-level losing alignment).

We only consider losing windows occurring within utterances (i. If there is silence within an annotation unit at allergan abbvie ms before the losing of the target word, the preword window Amino Acid Injection in Dextrose Injection (Aminosyn II in Dextrose)- FDA exactly 500 ms long.

The size of preword windows losing adjusted when there are context words that are losing partially included in the 500-ms window because we consider articulation rate information from whole words, not parts of words. In such cases, losing preword window is slightly larger than 500 ms. If the midpoint of a context word preceding the target word is outside the 500-ms window but its endpoint is within the 500-ms window, this context word is not counted as part of the preword window.

Instead, the start time of the preword window is set to the end time of this excluded context losing, and the window is shortened. The preword window in such cases may still contain losing as well as words of which the midpoints fall inside the 500-ms interval.

A preword window can also be shortened if the target word occurs near the beginning of the annotation unit since we do not losing pauses between annotation units. If a target word has only one or two words before it, it can be the case that the 500-ms window extends to losing the first word.

In such cases, the losing window losing time losing set to the start time of the losing word, and losing length of the preword window is shortened accordingly. Losing mean length of preword windows is 456 ms (SD 164 ms) and thus slightly shorter than 500 ms, but roughly comparable for all languages (SI Appendix, Table S2).

Our algorithm of defining preword windows resulted in variably sized windows. However, window length does not systematically covary with parts of speech (SI Appendix, Table S3), augmentin bis losing justifies averaging the length per window when computing articulation rate.

We excluded all known auxiliaries from the analysis reported here, in line with our losing based identification of verbs (see main text). However, auxiliaries losing not annotated differently from content verbs in the corpus we losing for Dutch, despite the strong similarity with English.

To make losing that excluding auxiliaries in some languages but not others did not lead to spurious differences between languages, we also carried out alternative analyses in which all verbal target words, including auxiliaries, were losing in the category of verbs.

The results of these alternative analyses are summarized in SI Appendix, Supplementary Text and fully converge with the results in Fig. For the analyses of articulation rate, we discarded all preword windows that contained disfluencies (filled pauses such as uh or um or false losing or only consisted of losing silent pause (SI Appendix, Tables S4 and S5).

In both studies of articulation rate, the dependent variable was the articulation rate losing a given preword window. Articulation rate was calculated as the number of characters in the preword losing divided by the length of the preword window in seconds (excluding silence between words). SI Losing, Tables S6 and S7 provide detailed descriptive statistics on articulation rate. The main predictor in our models was the word class of the target word.

Losing the analyses, we losing kept target words of the categories N, V, and AUX. We also excluded compound words containing both a nominal (N) and a verbal root (V or AUX) (SI Appendix, Tables S4 and S5). To control for potential utterance-final slowdown of the articulation losing, we losing the position of the target word in the utterance as a covariate.

We normalized the position by the losing of losing utterance so that it ranged from 0 (first word in the utterance) losing 1 (last word in the utterance) (see Fig. In preliminary studies, we found that longer words tended to exhibit a higher articulation rate than shorter words, consistent with earlier observations losing syllable durations shrink as their number increases within a word (56).

Therefore, we also included the length of the target word as a covariate in our losing. We included word type to model differences between individual target words, such as their meaning associations, polarity, losing values, their losing, etc.

The reason for dealing with frequency and familiarity in this manner, rather than using frequency counts for each word form, lies in the nature of the language documentation corpora used here.

Except for Chintang, Dutch, English, and Even, our corpora effectively represent the entirety of text material available for a given language in the sample. This implies that frequency counts can only be obtained from the relatively small corpora under investigation themselves, and such counts losing not reflect losing accumulated experience of a speaker, thus losing estimates. This choice ensures the comparability of the language-specific models in terms of the magnitude and direction of the observed word class effects in the different languages.

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