Toward detecting emotions in spoken dialogs

By Chi-Chun Lee, Shrikanth S. Narayanan. 2005

1,068 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Finally, environments in which affective dynamics and feelings play a role are recognized as important for human well being (De Botton, 2016; Lee and Narayanan, 2005).”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “Audio emotion recognition (also called SER) detects the embedded emotions by processing and understanding speech signals [161].”
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (2019, background): “Emotion Detection in Conversations: Early studies on emotion detection in conversations focus on call center dialogs using lexicon-based methods and audio features (Lee and Narayanan, 2005; Devillers and Vidrascu, 2006).”
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, methodology): “Principal Component Analysis (PCA) (Jolliﬀe, 2002) and Linear or Heteroscedastic Discriminant Analysis (LDA) (Fukunaga, 1990; Ayadi et al., 2007) are the most common techniques, used, e.g., in (Batliner et al., 2000; Kwon et al., 2003; Lee and Narayanan, 2005;Steidl, 2009).”
Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks (2014, methodology): “An alternative procedure for detecting emotions using lexical information is found in [18].”
Speech Emotion Recognition Using Fourier Parameters (2015, neutral): “Researchers have performed many studies [6]-[12].”
Automatic detection of learner’s affect from conversational cues (2008, background): “states, such as neutral, negative, and positive (Litman and Forbes-Riley 2004), negative versus positive/non-negative (Liscombe et al. 2005; Lee and Narayanan 2004), or annoyance versus frustration (Ang et al.”
Emotion Recognition (2009, background): “As found in the literature [1, 4], emotion in spoken dialogs is not limited to humanhuman interaction but is also found in human-computer interaction.”
Building Naturalistic Emotionally Balanced Speech Corpus by Retrieving Emotional Speech from Existing Podcast Recordings (2019, background): “results in over-emphasized expressions which differ from subtle behaviors observed during daily interactions [4], [7], [8], [9].”
ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors (2018, methodology): “modalities employed for AR include audio [14], [15], [16], visual [17], [18], [19] and audio-visual [20], [21], [22].”
Emotion recognition from speech: a review (2012, background): “The combination of language and discourse information is proposed in Lee and Narayanan (2005), for improving the discrimination between the positive and negative emotions, in the context of call center applications (Lee and Narayanan 2005).”
Paralinguistics in speech and language - State-of-the-art and the challenge (2013, methodology): “Linguistic information—well known to be useful (Lee and Narayanan, 2005)—could be used for the Affect Sub-Challenge, but the word level transcription is given exclusively for the Train and Develop sets of TUM AVIC, as the Challenge aims at ‘real-life’ conditions as if for a running system ‘in the wi”
Sentiment Adaptive End-to-End Dialog Systems (2018, background): “But there has been work on emotion detection in spoken dialog systems incorporating extra information as well (Lee and Narayanan, 2005; Devillers et al., 2003; Liscombe et al., 2005; Burkhardt et al., 2009; Yu et al., 2017).”
Analysis of emotion recognition using facial expressions, speech and multimodal information (2004, background): “Finally, other approaches have used language and discourse information, exploring the fact that some words are highly correlated with specific emotions [15].”
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception (2017, background): “This section overviews techniques to record emotional databases, emphasizing the role of recordings collected by actors.”
Databases, features and classifiers for speech emotion recognition: a review (2018, methodology): “Speech was recorded during machine human interaction e.g. during telephone calls to automatic speech recognition (ASR) call centers as discussed in Lee and Narayanan (2005).”
MoodScope: building a mood sensor from smartphone usage patterns (2013, neutral): “Existing work in affect recognition focuses primarily on the recognition of emotions [13, 14], and does so largely by leveraging the visual [15-17] and acoustic [18, 19] signals found in the speech, actions, and faces of people.”
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 (2012, background): “As identified by Lee and Narayanan (2005), further improvements can be made if the parameters of combined classifiers are globally optimized by jointly training the whole system.”