Affect-LM: A Neural Language Model for Customizable Affective Text Generation
Table 1: Summary of corpora used in this paper. CMU-MOSI and SEMAINE are observed to have higher emotional content than Fisher and DAIC corpora.
['[BOLD] Corpus Name', '[BOLD] Conversations', '[BOLD] Words', '[BOLD] % Colored Words', '[BOLD] Content']
The Fisher English Training Speech Corpus is the main corpus used for training the proposed model, in addition to which we have chosen three emotionally colored conversational corpora. Fisher English Training Speech Parts 1 & 2: The Fisher dataset Cieri et al. Each conversation is between two strangers who are requested to speak on a randomly selected topic from a set. Examples of conversation topics are Minimum Wage, Time Travel and Comedy. Distress Assessment Interview Corpus (DAIC): SEMAINE dataset: SEMAINE McKeown et al. There are a total of 959 conversations which are approximately 5 minutes each, and are transcribed and annotated with affective dimensions. Multimodal Opinion-level Sentiment Intensity Dataset (CMU-MOSI): Zadeh et al. This is a multimodal annotated corpus of opinion videos where in each video a speaker expresses his opinion on a commercial product. The corpus consist of speech from 93 videos from 89 distinct speakers (41 male and 48 female speakers). This corpus differs from the others since it contains monologues rather than conversations. While we find that all corpora contain spoken language, they have the following characteristics different from the Fisher corpus: ’s spontaneous replies to questions designed to generate an emotional response, or from conversations on emotion-inducing topics (2) Domain mismatch due to recording environment (for example, the DAIC corpus was created in a mental health setting, while the CMU-MOSI corpus consisted of opinion videos uploaded online). (3) Significantly smaller than the Fisher corpus, which is 25 times the size of the other corpora combined. Thus, we perform training in two separate stages - training of the baseline and Affect-LM models on the Fisher corpus, and subsequent adaptation and fine-tuning on each of the emotionally colored corpora.