  Personal narratives (PN) - spoken or written - are recollections of facts,
people, events, and thoughts from one's own experience. Emotion recognition and
sentiment analysis tasks are usually defined at the utterance or document
level. However, in this work, we focus on Emotion Carriers (EC) defined as the
segments (speech or text) that best explain the emotional state of the narrator
("loss of father", "made me choose"). Once extracted, such EC can provide a
richer representation of the user state to improve natural language
understanding and dialogue modeling. In previous work, it has been shown that
EC can be identified using lexical features. However, spoken narratives should
provide a richer description of the context and the users' emotional state. In
this paper, we leverage word-based acoustic and textual embeddings as well as
early and late fusion techniques for the detection of ECs in spoken narratives.
For the acoustic word-level representations, we use Residual Neural Networks
(ResNet) pretrained on separate speech emotion corpora and fine-tuned to detect
EC. Experiments with different fusion and system combination strategies show
that late fusion leads to significant improvements for this task.
