Recognition of para-linguistic information and its application to spoken dialogue system

Fujie, Shinya; Ejiri, Y.; Matsusaka, Yosuke; Kikuchi, Hideaki; Kobayashi, Tetsuo

doi:10.1109/asru.2003.1318446

Cited by 18 publications

(15 citation statements)

References 8 publications

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“…Other works deal with the identification of attitudes and intentions of the speaker. For example, Fujie et al (2003) report about the identification of positive/negative attitudes of the speaker, while Maekawa (2000) reports about the classification of paralinguistic items like admiration, suspicion, disappointment and indifference. In Hayashi (1999), paralinguistic items like affirmation, asking again, doubt and hesitation were also considered.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Paralinguistic Information using Prosodic Features Related to Intonation and Voice Quality

Ishi¹

2008

Speech Recognition

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IntroductionBesides the linguistic (verbal) information conveyed by speech, the paralinguistic (nonverbal) information, such as intenning the classification of paralinguistic information. Among the several paralinguistic items extions, attitudes and emotions expressed by the speaker, also convey important meanings in communication. Therefore, to realize a smooth communication between humans and spoken dialogue systems (such as robots), it becomes important to consider both linguistic and paralinguistic information. There is a lot of past research concerpressing intentions, attitudes and emotions, most previous research has focused on the classification of the basic emotions, such as anger, happiness and sadness (e.g., Fernandez et al., 2005;Schuller et al., 2005;Nwe et al., 2003;Neiberg et al., 2006). Other works deal with the identification of attitudes and intentions of the speaker. For example, Fujie et al. (2003) report about the identification of positive/negative attitudes of the speaker, while Maekawa (2000) reports about the classification of paralinguistic items like admiration, suspicion, disappointment and indifference. In Hayashi (1999), paralinguistic items like affirmation, asking again, doubt and hesitation were also considered. In the present work, aiming at smooth communication in dialogue between humans and spoken dialogue systems, we consider a variety of paralinguistic information, including intentions, attitudes and emotions, rather than limiting our focus to the basic emotions. The understanding of paralinguistic information becomes as important as linguistic information in spoken dialogue systems, especially in interjections such as "eh", "ah", and "un". Such interjections are frequently used to express a reaction to the conversation partner in a dialogue scenario in Japanese, conveying some information about the speaker's intention, attitude, or emotion. As there is little phonetic information represented by such interjections, most of the paralinguistic information is thought to be conveyed by its speaking style, which can be described by variations in prosodic features, including voice quality features. So far, most previous research dealing with paralinguistic information extraction has focused only on intonation-related prosodic features, using fundamental frequency (F0), power and duration (e.g

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Section: Introductionmentioning

confidence: 99%

Recognition of Paralinguistic Information using Prosodic Features Related to Intonation and Voice Quality

Ishi¹

2008

Speech Recognition

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“…Nevertheless, a disadvantage of using the eyes as features for motion estimation, is the inability to detect and track faces with sunglasses (commonly worn by visually impaired people). Gunes & Pantic [11] and Fujie et al [12] estimated head motion using optical flow. More precisely, the head region was extracted by skin color segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…A common approach for this is to use HMMs to recognize each gesture. In fact, all the systems described before make use of two or three HMMs with different states [6][7][8][9][11][12][13], e.g., up/down for nodding and left/right for shaking. This approach works well if the system is in a fixed position (i.e., third-party perspective), and is commonly use in applications such as robotics and Human Computer Interaction (HCI), where the system should be able to infer the proper gestures and react accordingly.…”

Section: Related Workmentioning

confidence: 99%

Robust Head Gestures Recognition for Assistive Technology

Terven

Salas

Raducanu

2014

Lecture Notes in Computer Science

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Abstract. This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.

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“…Imai et al 2001;Fujie et al 2004), linguistic and paralinguistic cues (e.g. Matsusaka et al 2003;Fujie et al 2005) or emotive vocalization (e.g. Cahn 1990; Abadjieva et al 1993), social touch-based communication (e.g.…”

Section: Introductionmentioning

confidence: 99%

Role of expressive behaviour for robots that learn from people

Breazeal¹

2009

Phil. Trans. R. Soc. B

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Robotics has traditionally focused on developing intelligent machines that can manipulate and interact with objects. The promise of personal robots, however, challenges researchers to develop socially intelligent robots that can collaborate with people to do things. In the future, robots are envisioned to assist people with a wide range of activities such as domestic chores, helping elders to live independently longer, serving a therapeutic role to help children with autism, assisting people undergoing physical rehabilitation and much more. Many of these activities shall require robots to learn new tasks, skills and individual preferences while 'on the job' from people with little expertise in the underlying technology. This paper identifies four key challenges in developing social robots that can learn from natural interpersonal interaction. The author highlights the important role that expressive behaviour plays in this process, drawing on examples from the past 8 years of her research group, the Personal Robots Group at the MIT Media Lab.

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Recognition of para-linguistic information and its application to spoken dialogue system

Cited by 18 publications

References 8 publications

Recognition of Paralinguistic Information using Prosodic Features Related to Intonation and Voice Quality

Recognition of Paralinguistic Information using Prosodic Features Related to Intonation and Voice Quality

Robust Head Gestures Recognition for Assistive Technology

Role of expressive behaviour for robots that learn from people

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