Factors Affecting User Perception of a Spoken Language vs. Textual Search Interface: A Content Analysis

Begany, Grace M.; Sa, Ning; Yuan, Xiaojun

doi:10.1093/iwc/iwv029

Cited by 24 publications

(28 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results indicate that when given the opportunity, the participants still make use of the "presubmission query modification" feature to ensure the quality of their search, which coincides with the results of Begany et al (2016) and should be taken into consideration in the design of future voice search systems. In traditional textual information retrieval, before submitting the query, the users have the chance to modify the text in the query box.…”

Section: Complete Query Versus Partial Query Modificationmentioning

confidence: 86%

“…That will result in the system searching on the wrong query and the users must wait until the system finishes search. Our results indicate that when given the opportunity, the participants still make use of the "presubmission query modification" feature to ensure the quality of their search, which coincides with the results of Begany et al (2016) and should be taken into consideration in the design of future voice search systems. In this study, the "presubmission query modification" is supported by introducing a "search" button which is pressed by the participants to initiate the search.…”

Section: Complete Query Versus Partial Query Modificationmentioning

confidence: 86%

“…According to Guy (2016), voice queries were closer to natural language corpuses that were longer and better-formed. Indeed, voice queries were found to contain more stop words (Crestani & Du, 2006), asked more "WH" and "how" questions (Begany et al, 2016;Schalkwyk et al, 2010), and used richer language such as "take me to" and "please" (Guy, 2016). These extra words may not be useful as part of a query.…”

Section: Query Behavior and Query Reformulation With Voice Inputmentioning

confidence: 99%

“…Begany et al (2016) reported that users would like to "be able to edit or partially delete the queries in the spoken system." However, most of the reformulation strategies were extracted by comparing the reformulated query and the previous query instead of concentrating on how users performed the reformulation.…”

Section: Query Behavior and Query Reformulation With Voice Inputmentioning

confidence: 99%

“…In earlier work, Schalkwyk et al (2010) and Kamvar and Beeferman (2010) investigated users' voice search behaviors using log-based data. Some interesting findings include: (a) task type has a significant impact on user query behavior using a spoken language interface ; (b) users' familiarity with the system, ease-of-use of the system, speed of the system, as well as trust, comfort level, fun factor, and novelty were all factors that can affect users' perception of the spoken system (Begany, Sa, & Yuan, 2016); (c) using the spoken interface resulted in significantly less interaction, in terms of number of iterations and number of saved and viewed webpages, than using the baseline system ; and (d) spoken queries are longer than textual queries , which agrees with Crestani and Du (2006). Yuan and her colleagues have examined how people respond to a spoken language interface versus a generic textual interface in a task environment .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Examining users' partial query modification patterns in voice search

Yuan

2019

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

This article investigates how to improve the effectiveness of voice search systems. Earlier research found that participants employed voice search much less frequently than keyboard search. The main reasons that participants disliked voice search are system mistakes and the inability to modify queries. In keyboard search, query reformulation is facilitated by partial query modification, which is not supported by most of the current voice search systems. Consequently, users need to speak the complete query in voice search even with only minor changes. This article focuses on examining partial query modification during voice search through a Wizard of Oz user experiment. It examines if users would prefer partial query modification and how they perform it in voice search. Thirty-two participants participated in the experiment. Results indicated that when given the opportunity, the users performed more partial query modifications than complete queries. Common partial query modification strategies and patterns emerged from the experiment. The results can be used to improve the voice search system design and benefit the research community in general. System implications and future work were discussed.

show abstract

Section: Complete Query Versus Partial Query Modificationmentioning

confidence: 86%

Section: Complete Query Versus Partial Query Modificationmentioning

confidence: 86%

Section: Query Behavior and Query Reformulation With Voice Inputmentioning

confidence: 99%

Section: Query Behavior and Query Reformulation With Voice Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Examining users' partial query modification patterns in voice search

Yuan

2019

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

show abstract

Intelligent In‐Vehicle Interaction Technologies

Murali

Kaboli

Dahiya

2021

Advanced Intelligent Systems

View full text Add to dashboard Cite

Autonomous vehicles (AVs) will soon become a reality with almost every major automotive original equipment manufacturer (OEM) such as Tesla, BMW, Daimler, and big technology giants such as Google's Waymo and Apple Car pushing toward the full self-driving objective. According to the SAE J3016 taxonomy [1] of autonomous driving, there are six levels of automated driving systems ranging from level 0 (completely manual) to level 5 (full selfdriving [FSD]) automated systems that are expected to function in all geographic locations, all weather conditions, and under all conditions. The proposed benefits of intelligent vehicles include fewer road accidents, enhanced safety, reduced traffic congestion, effective commute time usage, and importantly enjoyable and comfortable ride. With increased autonomy, the drivers also assume the role of mere passengers who are engaged in nondriving activities and are unavailable to participate in traffic interactions. This would increase complexity in a mixed-autonomy traffic environment as interactions with pedestrians and cyclists are based on visual cues with the driver. Therefore, intelligent vehicles also need to autonomously interact with other traffic participants such as pedestrians and cyclists and other vehicles.Human-vehicle interaction (HVI) is closely related to the field of human-robot interaction (HRI). It entails the problem of understanding and shaping the interaction dynamics between humans and vehicles. Particularly, the domain of interaction involves the study of sensation, perception, information exchange, inference, and decision-making. [2] With increasing levels of automation, the driver will have more time and choice to perform various tasks other than driving and this opens new avenues for interaction. As a result, a growing number of sensors are being incorporated into the vehicles to understand the driver's and/or passenger's actions, emotions, and personal choices to offer the precise functionalities and services for an enjoyable ride. [3] Figure 1 shows the schematic of in-vehicle interaction consisting of various interactive interfaces and sensing modalities present in the vehicle as well as some modes of interaction such as gestures, speech, and eye gaze.The advances in HVI have been presented in previous review articles, which have mainly focused on the detection of particular characteristics for driving assistance such as driver attention detection, [4] emotion recognition, [5] drowsiness detection, [6] mental workload, [7] and so on. A few other articles have reviewed the interior designs of AVs to better support and interact with drivers and passengers. [8,9] Likewise, the user interfaces (UIs) and user experience (UX) of vehicle interiors have been studied in context with manual as well as automated driving. [10] A number of previous works have also studied the interaction with the external

show abstract

Improving the effectiveness of voice search systems through partial query modification

Yuan

2022

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

This paper addresses the importance of improving the effectiveness of voice search systems through partial query modification. A user-centered experiment was designed to compare the effectiveness of an experimental system using partial query modification feature to a baseline system in which users could issue complete queries only, with 32 participants each searching on eight different tasks. The results indicate that the participants spent significantly more time preparing the modification but significantly less time speaking the modification by using the experimental system than by using the baseline system. The participants found that the experimental system (a) was more effective, (b) gave them more control, (c) was easier for the search tasks, and (d) saved them time than the baseline system. The results contribute to improving future voice search system design and benefiting the research community in general. System implications and future work were discussed.

show abstract

Factors Affecting User Perception of a Spoken Language vs. Textual Search Interface: A Content Analysis

Cited by 24 publications

References 23 publications

Examining users' partial query modification patterns in voice search

Examining users' partial query modification patterns in voice search

Intelligent In‐Vehicle Interaction Technologies

Improving the effectiveness of voice search systems through partial query modification

Contact Info

Product

Resources

About