We examined prosodic entrainment in spoken dialogs separately for several dialog acts in cooperative and competitive games. Entrainment was measured for intonation features derived from a superpositional intonation stylization as well as for rhythm features. The found differences can be related to the cooperative or competitive nature of the game, as well as to dialog act properties as its intrinsic authority, supportiveness and distributional characteristics. In cooperative games dialog acts with a high authority given by knowledge and with a high frequency showed the most entrainment. The results are discussed amongst others with respect to the degree of active entrainment control in cooperative behavior.
Entrainment affects the choice of words @cite_58 @cite_64 @cite_36 and syntactic constructions @cite_28 @cite_72 @cite_6 . Entrainment is also observed in phonetic measures of speaking rate @cite_66 @cite_80 , intensity @cite_66 @cite_48 , voice quality @cite_74 , and pitch @cite_14 @cite_65 @cite_1 @cite_42 is reported in data from dialogues and from speech shadowing experiments. In these studies evidence for entrainment is derived from acoustic measurements @cite_74 @cite_59 , word and sentence form analyses @cite_5 , and from perceptual similarity ratings @cite_77 @cite_33 . Entrainment has also been measured on the basis of categorical intonation features as derived automatically @cite_12 or by manual labeling @cite_41 .
@cite_64 @cite_36 @cite_41 @cite_42 @cite_72 @cite_5 @cite_58 @cite_66 @cite_48 @cite_74 @cite_80 @cite_28 @cite_6 @cite_77 @cite_12 @cite_14 @cite_33 @cite_65 @cite_1 @cite_59
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Human conversants in dialog adjust their behavior to their conversational partner in many ways. In terms of language use, they adapt to their partners both lexically and syntactically, by using the same referring expressions or sentence structure. In this paper, we describe a natural language generator personage-primed, which can produce utterances entrained to a range of utterance features used in prior utterances by a human user, and represented in the discourse context. Personage-primed can entrain to the user’s referring expressions, tense-modality selection, verb and noun lexical selection, hedge and cue word choice, and syntactic template selection, or any combination of these. To our knowledge, there is no other NLG engine that can dynamically generate all these types of entrainment in any utterance. We report an experiment testing all possible combinations of entrainment in a particular discourse context in order to test whether some types of entrainment are preferred, either because they make the utterance more natural, or because humans perceive the system as more friendly. Our experimental results suggest that human judgements of naturalness are distinct from friendliness: entraining on a user’s hedges increase perceptions of friendliness while reducing naturalness, while entraining on user’s referring expressions, syntactic template selection and tense modal choices increase perceptions of both naturalness and friendliness. Entrainment is the propensity of conversational partners to align different aspects of their communicative behavior. In this study we present three novel measures of prosodic entrainment based on intonational contours as defined by the ToBI conventions for prosodic description. Each of these measures estimates the similarity of contours used by speakers in different ways: by means of the perplexity of n-gram models, the Levenshtein distance, and the Kullback-Leibler divergence measure. We report significant correlations between each of these measures and manual annotations of a number of social variables related to the level of engagement of speakers, in a corpus of task-oriented dialogues in Standard American English. Automatic recognition of a speaker’s emotions is a natural objective for research, but is difficult to gauge the level of performance that is currently attainable. We describe a study that offers a rough benchmark. Speech data came from five passages of about 100 syllables each. They had been selected following pilot studies because they were effective at evoking specific emotion fear, anger, happiness, sadness, and neutrality. 40 subjects were recorded reading them. A battery of 32 potentially relevant features was extracted using our ASSESS system. They were broadly speaking prosodic, derived from contours tracing the movement of intensity and pitch. They were input to statistical decision mechanisms, of two types. Discriminant analysis uses linear combinations of variables to separate samples that belong to different categories. There are reasons to suspect that linear combination will not be appropriate, so neural net classifiers were also considered. An automatic relevance determination procedure was used to identify the most relevant parameters. Discriminant analysis outperformed the neural networks. Using 90 of the data for training, and testing on the remaining 10 , a classification rate of 55 (+ 0.08 ) was achieved. The most useful predictors covered a variety of properties – intensity (relative to the start of the passage) and its spread; pitch spread; durations of silences, rises in intensity, and syllables; and a property related to the shape of ‘tunes’, the number of inflections in the F0 contour per tune. Many more variables were less important, but nevertheless contributed. For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models' ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50 accuracy on our coreference test set and 53.5 for coherence cohesion (compared to a non-contextual baseline of 50 ). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multi-encoding and decoding of two sentences leads to the best performance (72.5 for coreference and 57 for coherence cohesion), highlighting the importance of target-side context.