The Duration of a Turn Cannot be Used to Predict When It Ends (2022)

Abstract: Turn taking in conversation is a complex process. We still don’t know how listeners are able to anticipate the end of a speaker’s turn. Previous work focuses on prosodic, semantic, and non-verbal cues that a turn is coming to an end. In this paper, we look at simple measures of duration — time, word count, and syllable count — to see if we can exploit the duration of turns as a cue. We find strong evidence that these metrics are useless.

## introduction turn-taking is a fundamental aspect of dialogue. timing of turn initiation is critical. sometimes long pauses are socially relevant @xcite . sometimes people overlap in conversation without the reason being clear @xcite . when trouble occurs, people can pause to signal misunderstandings @xcite . but turn taking as a whole is not well understood. what is known is that the time between successive turns is generally very short-much shorter than can be attributed to simple reactions to a turn ending @xcite . what this means is that people must anticipate the end of a turn @xcite . if we are anticipating the end of a turn, then there must be some features of utterances that we use to predict their ending, enabling fluid turn transition. in artificial agents that engage in spoken dialogue, turn-taking often falls by the wayside, leading to stilted conversations with long delays between turns or interruptions at inappropriate times @xcite . typical human interactions with current conversational agents work uniformly sequentially, as the agent processes and responds to the human once the end of an utterance has been completed, and it does not expect interruptions or overlaps @xcite . in general, computers can process information much faster than humans, but we have not yet developed fluid turn-taking algorithms. humans prepare a one-word utterance in around 600ms @xcite . computers can perform much faster than this and their speed is still increasing. but if an agent doesn't know when a turn ends, fluidity can be compromised. for smooth turn taking, agents need to know how to time their contributions appropriately. previous research looks at lexical @xcite , semantic @xcite , prosodic @xcite , or non-verbal @xcite attributes of utterances in order to anticipate turn ends. each of these has its own merits and drawbacks. lexical boundaries are relatively easy to compute and reason about. semantic completion of an utterance makes logical sense for an end-point to a thought. prosodic cues can be computed quickly from the speech signal, and non-verbal cues are ripe for deep learning techniques @xcite . turn duration, however, has not been studied yet for its use as a cue in anticipating its end, despite its ready availability to any spoken dialogue system. intuitively, one would expect that the duration of a turn is a strong cue about its ending. it would be plausible to assume that the longer someone has been talking already, the higher the probability is that the speaker will end their turn. compare it to waiting for a bus -we tend to assume that the longer we have waited for the bus, the higher the probability that it will finally arrive. but this is only so when the duration of a turn is normatively constrained. however, looking at distribution of a large number of conversational turns in dutch, de @xcite found that the distribution of turn-duration looks suspiciously much like an exponential distribution. and a unique and counter-intuitive property of this distribution is that it has a constant hazard rate: no matter how long we have waited for the process to complete, the probability of it terminating in the next instant remains constant. if turn durations are in fact exponentially distributed, it would mean that the duration of a turn so far does not contain any information about its projected duration. however, the observation in de ruiter (2019) were only for one small corpus in dutch, and measured in milliseconds. it could be that measuring duration in other units, like words, syllables, or other turn-related units would show a different distribution. in this study we set out to study if this suspected property of turn durations is generalizable to a larger corpus in english, and to other units of duration. turns in dialogue are composed of turn construction units (tcus). tcus are bounded by transition relevant places @xcite . at each transition relevant place, another person could take the floor or the current speaker could continue. in this study, we will investigate the duration of both tcus and entire turns. as there may be social preferences regarding the number of tcus within a turn, we will also examine the usefulness of the number of tcus per turn in predicting floor transitions. in the following sections, we outline the data collection and our statistical analyses. then we will show the distributions of the data and the statistical models describing the data. we will then discuss the implications of our results, and present ideas on how these results can be used to improve spoken dialogue agents. ## results here we will report the basic findings of our analyses. the interpretation of the findings will be delayed to discussion. ## discussion the analyses above, when taken together, suggest that there is little to be learned from examining the length of utterances as a sole heuristic for predicting their end. as was suspected on theoretical grounds (de ruiter, 2019) there is very little information in simple duration. the work here extended this previous work from timing of tcus to examine semantic content as shown by word counts, phonetic information as shown by syllable counts, or social action as shown by tcus per turn. none of these linguistics frames showed any substantial departure from the constant hazard-rate distribution. there may be contexts where utterance length is a useful heuristic for tcu or turn end or situations in which the statistics describe here do not fit well. for example, one would suspect that different dialogue acts may lend themselves to different tcu lengths -short backchannels, for example. or, particular social situations may lend themselves to fewer tcus per turn to ensure participants maintain the same mental models. a follow-up study on (e.g.,) the map task corpus might show these deviation, if they exist. the largest deviation from the exponential model occurred in the turn word and syllable count analyses. these two results reflect the combination of high rates of single tcu turns and large shares of low tcu word and syllable counts. our tcu per turn analysis shows that single-tcu turns are more common than the exponetial model is able to fit, but the negative binomial distribution moves prob-ability mass to the tail, making it an even worse fit, both visually and statistically. short turns skew the data in turn-level word and syllable count toward very low counts as compared to the exponential model. we expected that the tcus per turn negative binary model would account for the high number of single tcu turns, much like the gamma model does for the other data views. however, the geometric model outperforms the negative binomial by the largest waic difference of any models discussed. therefore, we must conclude that the geometric model is the superior fit, and the best-fit hazard rate for each tcu-the chance that the speaker's turn is over at the end of any tcu-is 50%, or a coin flip. so, not only is the maximum entropy geometric model a better fit, but there is no reliable bias for whether a turn is over at the end of a tcu. ## conclusion in this paper, we first confirmed the suspicions raised in de ruiter (2019)-the duration of tcus follows an exponential distribution. we then extended these findings in several ways. first, tcus also follow this distribution by syllable or word count. conversation does not orient to the amount of phonological or semantic information. it follows that if these factors are useful for turn taking, they are useful based on their meaning and structure, not their quantity or base informational load. next, we expanded our findings to the turn level, rather than just tcus. turn duration, syllable, and word count findings were akin to those at the tcu level, and so we must draw the conclusions that these turn length measurements are not useful either to exploit as information source in turn taking. finally, we looked at tcus per turn for evidence that the number of dialogue acts of which a turn has numerical norms. the tcu per turn analysis showed that the end of a tcu is essentially a coin flip for whether there will be a floor transfer. not only did the maximum entropy distribution have the best fit, but the hazard rate was very close to 0.5. so, we must conclude that there is no more pragmatic pressure to end one's turn when it is already very long. our general conclusion therefore is that, surprisingly, the duration of turns are not useful cues for turn segmentation or turn taking decisions. this is independent of whether we use temporal, phonological, lexical, or tcu-based measures of infor-mation. agents that do turn taking will need to use linguistic or prosodic cues other than duration to achieve accurate timing in their turn taking behavior.