Review of spoken dialogue systems

López-Cózar, Ramón; Callejas, Zoraida; Griol, David; Quesada, José F.

doi:10.3989/loquens.2014.012

Cited by 20 publications

(13 citation statements)

References 81 publications

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“…More and more computers are now able to communicate with their users in natural language [1]. For example, spoken dialogue systems (SDS) serve as personal assistants and are implemented in smartphones (like Siri from Apple and Cortana5 from Microsoft), cars [2], or special devices (such as Echo from Amazon: [3]). SDS are also used in educational contexts, e.g., in learning programs for children [4].…”

Section: Technology That Interacts: Insights Into the Mechanismsmentioning

confidence: 99%

Student Evaluations of a (Rude) Spoken Dialogue System Insights from an Experimental Study

Jucks¹,

Linnemann²,

Brummernhenrich³

2018

Advances in Human-Computer Interaction

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Communicating with spoken dialogue systems (SDS) such as Apple’s Siri® and Google’s Now is becoming more and more common. We report a study that manipulates an SDS’s word use with regard to politeness. In an experiment, 58 young adults evaluated the spoken messages of our self-developed SDS as it replied to typical questions posed by university freshmen. The answers were either formulated politely or rudely. Dependent measures were both holistic measures of how students perceived the SDS as well as detailed evaluations of each single answer. Results show that participants not only evaluated the content of rude answers as being less appropriate and less pleasant than the polite answers, but also evaluated the rude system as less accurate. Lack of politeness also impacted aspects of the perceived trustworthiness of the SDS. We conclude that users of SDS expect such systems to be polite, and we then discuss some practical implications for designing SDS.

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Section: Technology That Interacts: Insights Into the Mechanismsmentioning

confidence: 99%

Student Evaluations of a (Rude) Spoken Dialogue System Insights from an Experimental Study

Jucks¹,

Linnemann²,

Brummernhenrich³

2018

Advances in Human-Computer Interaction

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“…A SDS is a computer program that engages the user in a conversation by accepting speech input from the user and producing speech output to accomplish a specific goal or task [24]. Essentially mirroring human-to-human turn-taking in a conversation.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [27] designed an automatic speech recognition (ASR) module with limited vocabulary for use in accessing personal emergency assistance by older adults on a mobile cell phone platform. An ASR is a basic component of all SDS [24, 28–30]. Kim et al [31] proposed a multi-modal approach to PER using inputs from vision, voice, and a body worn gravity sensor.…”

Section: Introductionmentioning

confidence: 99%

“…López-Cózar et al [24] asserts that to design and develop a well-functioning, dynamically adaptable SDS, it is necessary to have correct models of the end-user, and knowledge of the user states. This knowledge may include what utterances (e.g., words, phrases) the user might say (lexical semantics), how (s)he might speak these utterances (e.g., speaking style [34], paralinguistics [35]), the environment where or condition under which the dialogue occurs (situational context), the reason why the words were spoken (speaker intent), and what utterances might be spoken next (prediction) [24].…”

Section: Introductionmentioning

confidence: 99%

“…This knowledge may include what utterances (e.g., words, phrases) the user might say (lexical semantics), how (s)he might speak these utterances (e.g., speaking style [34], paralinguistics [35]), the environment where or condition under which the dialogue occurs (situational context), the reason why the words were spoken (speaker intent), and what utterances might be spoken next (prediction) [24]. Designing and developing a device by considering the end-user and the end-user environment is in-line with the theory of “user sensitive inclusive design,” an extension of the user-centred design paradigm.…”

Section: Introductionmentioning

confidence: 99%

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Exploratory analysis of real personal emergency response call conversations: considerations for personal emergency response spoken dialogue systems

Young

Rochon

Mihailidis

2016

J NeuroEngineering Rehabil

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BackgroundThe purpose of this study was to derive data from real, recorded, personal emergency response call conversations to help improve the artificial intelligence and decision making capability of a spoken dialogue system in a smart personal emergency response system. The main study objectives were to: develop a model of personal emergency response; determine categories for the model’s features; identify and calculate measures from call conversations (verbal ability, conversational structure, timing); and examine conversational patterns and relationships between measures and model features applicable for improving the system’s ability to automatically identify call model categories and predict a target response.MethodsThis study was exploratory and used mixed methods. Personal emergency response calls were pre-classified according to call model categories identified qualitatively from response call transcripts. The relationships between six verbal ability measures, three conversational structure measures, two timing measures and three independent factors: caller type, risk level, and speaker type, were examined statistically.ResultsEmergency medical response services were the preferred response for the majority of medium and high risk calls for both caller types. Older adult callers mainly requested non-emergency medical service responders during medium risk situations. By measuring the number of spoken words-per-minute and turn-length-in-words for the first spoken utterance of a call, older adult and care provider callers could be identified with moderate accuracy. Average call taker response time was calculated using the number-of-speaker-turns and time-in-seconds measures. Care providers and older adults used different conversational strategies when responding to call takers. The words ‘ambulance’ and ‘paramedic’ may hold different latent connotations for different callers.ConclusionsThe data derived from the real personal emergency response recordings may help a spoken dialogue system classify incoming calls by caller type with moderate probability shortly after the initial caller utterance. Knowing the caller type, the target response for the call may be predicted with some degree of probability and the output dialogue could be tailored to this caller type. The average call taker response time measured from real calls may be used to limit the conversation length in a spoken dialogue system before defaulting to a live call taker.

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Trust the Words: Insights into the Role of Language in Trust Building in a Digitalized World

et al. 2016

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Review of spoken dialogue systems

Cited by 20 publications

References 81 publications

Student Evaluations of a (Rude) Spoken Dialogue System Insights from an Experimental Study

Student Evaluations of a (Rude) Spoken Dialogue System Insights from an Experimental Study

Exploratory analysis of real personal emergency response call conversations: considerations for personal emergency response spoken dialogue systems

Trust the Words: Insights into the Role of Language in Trust Building in a Digitalized World

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