666 research outputs found
Vocal accommodation in human-computer interaction : modeling and integration into spoken dialogue systems
With the rapidly increasing usage of voice-activated devices worldwide, verbal communication with computers is steadily becoming more common. Although speech is the principal natural manner of human communication, it is still challenging for computers, and users had been growing accustomed to adjusting their speaking style for computers. Such adjustments occur naturally, and typically unconsciously, in humans during an exchange to control the social distance between the interlocutors and improve the conversationâs efficiency. This phenomenon is called accommodation and it occurs on various modalities in human communication, like hand gestures, facial expressions, eye gaze, lexical and grammatical choices, and others. Vocal accommodation deals with phonetic-level changes occurring in segmental and suprasegmental features. A decrease in the difference between the speakersâ feature realizations results in convergence, while an increasing distance leads to divergence. The lack of such mutual adjustments made naturally by humans in computersâ speech creates a gap between human-human and human-computer interactions. Moreover, voice-activated systems currently speak in exactly the same manner to all users, regardless of their speech characteristics or realizations of specific features. Detecting phonetic variations and generating adaptive speech output would enhance user personalization, offer more human-like communication, and ultimately should improve the overall interaction experience. Thus, investigating these aspects of accommodation will help to understand and improving human-computer interaction. This thesis provides a comprehensive overview of the required building blocks for a roadmap toward the integration of accommodation capabilities into spoken dialogue systems. These include conducting human-human and human-computer interaction experiments to examine the differences in vocal behaviors, approaches for modeling these empirical findings, methods for introducing phonetic variations in synthesized speech, and a way to combine all these components into an accommodative system. While each component is a wide research field by itself, they depend on each other and hence should be jointly considered. The overarching goal of this thesis is therefore not only to show how each of the aspects can be further developed, but also to demonstrate and motivate the connections between them. A special emphasis is put throughout the thesis on the importance of the temporal aspect of accommodation. Humans constantly change their speech over the course of a conversation. Therefore, accommodation processes should be treated as continuous, dynamic phenomena. Measuring differences in a few discrete points, e.g., beginning and end of an interaction, may leave many accommodation events undiscovered or overly smoothed. To justify the effort of introducing accommodation in computers, it should first be proven that humans even show any phonetic adjustments when talking to a computer as they do with a human being. As there is no definitive metric for measuring accommodation and evaluating its quality, it is important to empirically study humans productions to later use as references for possible behaviors. In this work, this investigation encapsulates different experimental configurations to achieve a better picture of accommodation effects. First, vocal accommodation was inspected where it naturally occurs, namely in spontaneous human-human conversations. For this purpose, a collection of real-world sales conversations, each with a different representative-prospect pair, was collected and analyzed. These conversations offer a glance into accommodation effects in authentic, unscripted interactions with the common goal of negotiating a deal on the one hand, but with the individual facet of each side of trying to get the best terms on the other hand. The conversations were analyzed using cross-correlation and time series techniques to capture the change dynamics over time. It was found that successful conversations are distinguishable from failed ones by multiple measures. Furthermore, the sales representative proved to be better at leading the vocal changes, i.e., making the prospect follow their speech styles rather than the other way around. They also showed a stronger tendency to take that lead at an earlier stage, all the more so in successful conversations. The fact that accommodation occurs more by trained speakers and improves their performances fits anecdotal best practices of sales experts, which are now also proven scientifically. Following these results, the next experiment came closer to the final goal of this work and investigated vocal accommodation effects in human-computer interaction. This was done via a shadowing experiment, which offers a controlled setting for examining phonetic variations. As spoken dialogue systems with such accommodation capabilities (like this work aims to achieve) do not exist yet, a simulated system was used to introduce these changes to the participants, who believed they help with the testing of a language learning tutoring system. After determining their preference concerning three segmental phonetic features, participants were listen-ing to either natural or synthesized voices of male and female speakers, which produced the participantsâ dispreferred variation of the aforementioned features. Accommodation occurred in all cases, but the natural voices triggered stronger effects. Nevertheless, it can be concluded that participants were accommodating toward synthetic voices as well, which means that social mechanisms are applied in humans also when speaking with computer-based interlocutors. The shadowing paradigm was utilized also to test whether accommodation is a phenomenon associated only with speech or with other vocal productions as well. To that end, accommodation in the singing of familiar and novel music was examined. Interestingly, accommodation was found in both cases, though in different ways. While participants seemed to use the familiar piece merely as a reference for singing more accurately, the novel piece became the goal for complete replicate. For example, one difference was that mostly pitch corrections were introduced in the former case, while in the latter also key and rhythmic patterns were adopted. Some of those findings were expected and they show that peopleâs more salient features are also harder to modify using external auditory influence. Lastly, a multiparty experiment with spontaneous human-human-computer interactions was carried out to compare accommodation in human-directed and computer-directed speech. The participants solved tasks for which they needed to talk both with a confederate and with an agent. This allows a direct comparison of their speech based on the addressee within the same conversation, which has not been done so far. Results show that some participantsâ vocal behavior changed similarly when talking to the confederate and the agent, while othersâ speech varied only with the confederate. Further analysis found that the greatest factor for this difference was the order in which the participants talked with the interlocutors. Apparently, those who first talked to the agent alone saw it more as a social actor in the conversation, while those who interacted with it after talking to the confederate treated it more as a means to achieve a goal, and thus behaved differently with it. In the latter case, the variations in the human-directed speech were much more prominent. Differences were also found between the analyzed features, but the task type did not influence the degree of accommodation effects. The results of these experiments lead to the conclusion that vocal accommodation does occur in human-computer interactions, even if often to lesser degrees. With the question of whether people accommodate to computer-based interlocutors as well answered, the next step would be to describe accommodative behaviors in a computer-processable manner. Two approaches are proposed here: computational and statistical. The computational model aims to capture the presumed cognitive process associated with accommodation in humans. This comprises various steps, such as detecting the variable featureâs sound, adding instances of it to the featureâs mental memory, and determining how much the sound will change while taking into account both its current representation and the external input. Due to its sequential nature, this model was implemented as a pipeline. Each of the pipelineâs five steps corresponds to a specific part of the cognitive process and can have one or more parameters to control its output (e.g., the size of the featureâs memory or the accommodation pace). Using these parameters, precise accommodative behaviors can be crafted while applying expert knowledge to motivate the chosen parameter values. These advantages make this approach suitable for experimentation with pre-defined, deterministic behaviors where each step can be changed individually. Ultimately, this approach makes a system vocally responsive to usersâ speech input. The second approach grants more evolved behaviors, by defining different core behaviors and adding non-deterministic variations on top of them. This resembles human behavioral patterns, as each person has a base way of accommodating (or not accommodating), which may arbitrarily change based on the specific circumstances. This approach offers a data-driven statistical way to extract accommodation behaviors from a given collection of interactions. First, the target featureâs values of each speaker in an interaction are converted into continuous interpolated lines by drawing one sample from the posterior distribution of a Gaussian process conditioned on the given values. Then, the gradients of these lines, which represent rates of mutual change, are used to defined discrete levels of change based on their distribution. Finally, each level is assigned a symbol, which ultimately creates a symbol sequence representation for each interaction. The sequences are clustered so that each cluster stands for a type of behavior. The sequences of a cluster can then be used to calculate n-gram probabilities that enable the generation of new sequences of the captured behavior. The specific output value is sampled from the range corresponding to the generated symbol. With this approach, accommodation behaviors are extracted directly from data, as opposed to manually crafting them. However, it is harder to describe what exactly these behaviors represent and motivate the use of one of them over the other. To bridge this gap between these two approaches, it is also discussed how they can be combined to benefit from the advantages of both. Furthermore, to generate more structured behaviors, a hierarchy of accommodation complexity levels is suggested here, from a direct adoption of usersâ realizations, via specified responsiveness, and up to independent core behaviors with non-deterministic variational productions. Besides a way to track and represent vocal changes, an accommodative system also needs a text-to-speech component that is able to realize those changes in the systemâs speech output. Speech synthesis models are typically trained once on data with certain characteristics and do not change afterward. This prevents such models from introducing any variation in specific sounds and other phonetic features. Two methods for directly modifying such features are explored here. The first is based on signal modifications applied to the output signal after it was generated by the system. The processing is done between the timestamps of the target features and uses pre-defined scripts that modify the signal to achieve the desired values. This method is more suitable for continuous features like vowel quality, especially in the case of subtle changes that do not necessarily lead to a categorical sound change. The second method aims to capture phonetic variations in the training data. To that end, a training corpus with phonemic representations is used, as opposed to the regular graphemic representations. This way, the model can learn more direct relations between phonemes and sound instead of surface forms and sound, which, depending on the language, might be more complex and depend on their surrounding letters. The target variations themselves donât necessarily need to be explicitly present in the training data, all time the different sounds are naturally distinguishable. In generation time, the current target featureâs state determines the phoneme to use for generating the desired sound. This method is suitable for categorical changes, especially for contrasts that naturally exist in the language. While both methods have certain limitations, they provide a proof of concept for the idea that spoken dialogue systems may phonetically adapt their speech output in real-time and without re-training their text-to-speech models. To combine the behavior definitions and the speech manipulations, a system is required, which can connect these elements to create a complete accommodation capability. The architecture suggested here extends the standard spoken dialogue system with an additional module, which receives the transcribed speech signal from the speech recognition component without influencing the input to the language understanding component. While language the understanding component uses only textual transcription to determine the userâs intention, the added component process the raw signal along with its phonetic transcription. In this extended architecture, the accommodation model is activated in the added module and the information required for speech manipulation is sent to the text-to-speech component. However, the text-to-speech component now has two inputs, viz. the content of the systemâs response coming from the language generation component and the states of the defined target features from the added component. An implementation of a web-based system with this architecture is introduced here, and its functionality is showcased by demonstrating how it can be used to conduct a shadowing experiment automatically. This has two main advantage: First, since the system recognizes the participantsâ phonetic variations and automatically selects the appropriate variation to use in its response, the experimenter saves time and prevents manual annotation errors. The experimenter also automatically gains additional information, like exact timestamps of utterances, real-time visualization of the interlocutorsâ productions, and the possibility to replay and analyze the interaction after the experiment is finished. The second advantage is scalability. Multiple instances of the system can run on a server and be accessed by multiple clients at the same time. This not only saves time and the logistics of bringing participants into a lab, but also allows running the experiment with different configurations (e.g., other parameter values or target features) in a controlled and reproducible way. This completes a full cycle from examining human behaviors to integrating accommodation capabilities. Though each part of it can undoubtedly be further investigated, the emphasis here is on how they depend and connect to each other. Measuring changes features without showing how they can be modeled or achieving flexible speech synthesis without considering the desired final output might not lead to the final goal of introducing accommodation capabilities into computers. Treating accommodation in human-computer interaction as one large process rather than isolated sub-problems lays the ground for more comprehensive and complete solutions in the future.Heutzutage wird die verbale Interaktion mit Computern immer gebrĂ€uchlicher, was der rasant wachsenden Anzahl von sprachaktivierten GerĂ€ten weltweit geschuldet ist. Allerdings stellt die computerseitige Handhabung gesprochener Sprache weiterhin eine groĂe Herausforderung dar, obwohl sie die bevorzugte Art zwischenmenschlicher Kommunikation reprĂ€sentiert. Dieser Umstand fuÌhrt auch dazu, dass Benutzer ihren Sprachstil an das jeweilige GerĂ€t anpassen, um diese Handhabung zu erleichtern. Solche Anpassungen kommen in menschlicher gesprochener Sprache auch in der zwischenmenschlichen Kommunikation vor. Ăblicherweise ereignen sie sich unbewusst und auf natuÌrliche Weise wĂ€hrend eines GesprĂ€chs, etwa um die soziale Distanz zwischen den GesprĂ€chsteilnehmern zu kontrollieren oder um die Effizienz des GesprĂ€chs zu verbessern. Dieses PhĂ€nomen wird als Akkommodation bezeichnet und findet auf verschiedene Weise wĂ€hrend menschlicher Kommunikation statt. Sie Ă€uĂert sich zum Beispiel in der Gestik, Mimik, Blickrichtung oder aber auch in der Wortwahl und dem verwendeten Satzbau. Vokal- Akkommodation beschĂ€ftigt sich mit derartigen Anpassungen auf phonetischer Ebene, die sich in segmentalen und suprasegmentalen Merkmalen zeigen. Werden AusprĂ€gungen dieser Merkmale bei den GesprĂ€chsteilnehmern im Laufe des GesprĂ€chs Ă€hnlicher, spricht man von Konvergenz, vergröĂern sich allerdings die Unterschiede, so wird dies als Divergenz bezeichnet. Dieser natuÌrliche gegenseitige Anpassungsvorgang fehlt jedoch auf der Seite des Computers, was zu einer LuÌcke in der Mensch-Maschine-Interaktion fuÌhrt. DaruÌber hinaus verwenden sprachaktivierte Systeme immer dieselbe Sprachausgabe und ignorieren folglich etwaige Unterschiede zum Sprachstil des momentanen Benutzers. Die Erkennung dieser phonetischen Abweichungen und die Erstellung von anpassungsfĂ€higer Sprachausgabe wuÌrden zur Personalisierung dieser Systeme beitragen und könnten letztendlich die insgesamte Benutzererfahrung verbessern. Aus diesem Grund kann die Erforschung dieser Aspekte von Akkommodation helfen, Mensch-Maschine-Interaktion besser zu verstehen und weiterzuentwickeln. Die vorliegende Dissertation stellt einen umfassenden Ăberblick zu Bausteinen bereit, die nötig sind, um AkkommodationsfĂ€higkeiten in Sprachdialogsysteme zu integrieren. In diesem Zusammenhang wurden auch interaktive Mensch-Mensch- und Mensch- Maschine-Experimente durchgefuÌhrt. In diesen Experimenten wurden Differenzen der vokalen Verhaltensweisen untersucht und Methoden erforscht, wie phonetische Abweichungen in synthetische Sprachausgabe integriert werden können. Um die erhaltenen Ergebnisse empirisch auswerten zu können, wurden hierbei auch verschiedene ModellierungsansĂ€tze erforscht. Fernerhin wurde der Frage nachgegangen, wie sich die betreffenden Komponenten kombinieren lassen, um ein Akkommodationssystem zu konstruieren. Jeder dieser Aspekte stellt fuÌr sich genommen bereits einen uÌberaus breiten Forschungsbereich dar. Allerdings sind sie voneinander abhĂ€ngig und sollten zusammen betrachtet werden. Aus diesem Grund liegt ein uÌbergreifender Schwerpunkt dieser Dissertation darauf, nicht nur aufzuzeigen, wie sich diese Aspekte weiterentwickeln lassen, sondern auch zu motivieren, wie sie zusammenhĂ€ngen. Ein weiterer Schwerpunkt dieser Arbeit befasst sich mit der zeitlichen Komponente des Akkommodationsprozesses, was auf der Beobachtung fuĂt, dass Menschen im Laufe eines GesprĂ€chs stĂ€ndig ihren Sprachstil Ă€ndern. Diese Beobachtung legt nahe, derartige Prozesse als kontinuierliche und dynamische Prozesse anzusehen. Fasst man jedoch diesen Prozess als diskret auf und betrachtet z.B. nur den Beginn und das Ende einer Interaktion, kann dies dazu fuÌhren, dass viele Akkommodationsereignisse unentdeckt bleiben oder uÌbermĂ€Ăig geglĂ€ttet werden. Um die Entwicklung eines vokalen Akkommodationssystems zu rechtfertigen, muss zuerst bewiesen werden, dass Menschen bei der vokalen Interaktion mit einem Computer ein Ă€hnliches Anpassungsverhalten zeigen wie bei der Interaktion mit einem Menschen. Da es keine eindeutig festgelegte Metrik fuÌr das Messen des Akkommodationsgrades und fuÌr die Evaluierung der AkkommodationsqualitĂ€t gibt, ist es besonders wichtig, die Sprachproduktion von Menschen empirisch zu untersuchen, um sie als Referenz fuÌr mögliche Verhaltensweisen anzuwenden. In dieser Arbeit schlieĂt diese Untersuchung verschiedene experimentelle Anordnungen ein, um einen besseren Ăberblick uÌber Akkommodationseffekte zu erhalten. In einer ersten Studie wurde die vokale Akkommodation in einer Umgebung untersucht, in der sie natuÌrlich vorkommt: in einem spontanen Mensch-Mensch GesprĂ€ch. Zu diesem Zweck wurde eine Sammlung von echten VerkaufsgesprĂ€chen gesammelt und analysiert, wobei in jedem dieser GesprĂ€che ein anderes Handelsvertreter-Neukunde Paar teilgenommen hatte. Diese GesprĂ€che verschaffen einen Einblick in Akkommodationseffekte wĂ€hrend spontanen authentischen Interaktionen, wobei die GesprĂ€chsteilnehmer zwei Ziele verfolgen: zum einen soll ein GeschĂ€ft verhandelt werden, zum anderen möchte aber jeder Teilnehmer fuÌr sich die besten Bedingungen aushandeln. Die Konversationen wurde durch das Kreuzkorrelation-Zeitreihen-Verfahren analysiert, um die dynamischen Ănderungen im Zeitverlauf zu erfassen. Hierbei kam zum Vorschein, dass sich erfolgreiche Konversationen von fehlgeschlagenen GesprĂ€chen deutlich unterscheiden lassen. Ăberdies wurde festgestellt, dass die Handelsvertreter die treibende Kraft von vokalen Ănderungen sind, d.h. sie können die Neukunden eher dazu zu bringen, ihren Sprachstil anzupassen, als andersherum. Es wurde auch beobachtet, dass sie diese Akkommodation oft schon zu einem fruÌhen Zeitpunkt auslösen, was besonders bei erfolgreichen GesprĂ€chen beobachtet werden konnte. Dass diese Akkommodation stĂ€rker bei trainierten Sprechern ausgelöst wird, deckt sich mit den meist anekdotischen Empfehlungen von erfahrenen Handelsvertretern, die bisher nie wissenschaftlich nachgewiesen worden sind. Basierend auf diesen Ergebnissen beschĂ€fti
Directional adposition use in English, Swedish and Finnish
Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellÀ (in front of) and jÀljessÀ (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003).
When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellÀ (in front of) and jÀljessÀ (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected.
We asked native English, Swedish and Finnish speakersâ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers.
All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion.
We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion.
Levinson, S. C. (1996). Frames of reference and Molyneuxâs question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press.
Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press.
Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo
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Gender differences in navigation dialogues with computer systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Gender is among the most influential of the factors underlying differences in spatial abilities, human communication and interactions with and through computers. Past research has offered important insights into gender differences in navigation and language use. Yet, given the multidimensionality of these domains, many issues remain contentious while others unexplored. Moreover, having been derived from non-interactive, and often artificial, studies, the generalisability of this research to interactive contexts of use, particularly in the practical domain of Human-Computer Interaction (HCI), may be problematic. At the same time, little is known about how gender strategies, behaviours and preferences interact with the features of technology in various domains of HCI, including collaborative systems and systems with natural language interfaces. Targeting these knowledge gaps, the thesis aims to address the central question of how gender differences emerge and operate in spatial navigation dialogues with computer systems.
To this end, an empirical study is undertaken, in which, mixed-gender and same-gender pairs communicate to complete an urban navigation task, with one of the participants being under the impression that he/she interacts with a robot. Performance and dialogue data were collected using a custom system that supported synchronous navigation and communication between the user and the robot.
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Proceedings of the LREC 2018 Special Speech Sessions
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Shared Perception in Human-Robot Interaction
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