The extracted feature vectors for each of the speech utterance made by the speaker are used for modeling the speaker using the GMM modeling technique as described in Section 4.3 for different Gaussian mixture sizes of 16, 32, 64, 128, and 256.
Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
Sample intent: method
Sample text:```For example, Kawato et al. (1990) suggested that movement trajectories can be projected using critical via-points through which the trajectory has to pass at a certain time, rather than in terms of the moment-by-moment dynamics of the implemented movement trajectory. Optimal trajectories can then be learnt by applying local optimising principles for getting from one via-point to the next. In this way, impoverished predictions can be used to monitor rich implementations. In the same way, language users might predict particularly crucial aspects of an utterance, but the aspects that they predict will depend on the circumstances. More generally, Meyer & Hagoort question the value of predicting one’s own utterance. They argue that prediction is useful when it is likely to differ from the actual event. This is the case when predicting another person’s behaviour or when the result of one’s action is uncertain (e.g., moving in a strong wind). But they argue that people are confident about their own speech. Meyer & Hagoort admit that they will tend to be less confident in dialogue, and we agree. But more important, we argue that the behaviour of the production implementer is not fully determined by the production command, because the complex processes involved in production are subject to internal (“neural”) noise or priming (i.e., influences that may not be a result of the production command). Assuming that these sources of noise do not necessarily affect forward modelling as well, predicted speech may differ from actual speech. In addition, prediction is useful even if the behaviour is fully predictable, because it allows the actor to plan future behaviour on the basis of the prediction. In fact, we made such a proposal in relation to the order of heavy and light phrases (see sect. 3.1, target article). Hartsuiker claims that our account incorrectly predicts an early competitor effect in Huettig and Hartsuiker (2010). His claim is based on the assumption that Response/Pickering & Garrod: An integrated theory of language production and comprehension```
Sample intent: method



Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
Sample intent: background



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```Our heritability result is generally in agreement with the studies of Kien et al. (2008) in Eucalyptus urophylla and Zhao et al. (2014a) in poplar.```
Sample intent: result
Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
Sample intent: method



Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```We use BLEU (up to 4-grams) (Papineni et al., 2002) as evaluation metric, which computes the precision of n-grams in generated summaries with gold-standard abstracts as the reference.```
Sample intent: background



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



Sample text:```The GITT model performs better than an empirical lag-time model [17], however, its predicted profiles tend to underestimate Cmax, which, if consistent, may suggest confounding systematic bias or invalid assumptions.```
Sample intent: background



Sample text:```Our heritability result is generally in agreement with the studies of Kien et al. (2008) in Eucalyptus urophylla and Zhao et al. (2014a) in poplar.```
Sample intent: result



Sample text:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
Sample intent: method



Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
Sample intent: method