9 accepted papers
Accurate speech emotion recognition is essential for developing human-facing systems. Recent advancements have included finetuning large, pretrained transformer models like Wav2Vec 2.0. However, the finetuning process requires substantial computational resources, including high-memory GPUs and signi…
Paralinguistics, the non-lexical components of speech, play a crucial role in human-human interaction. Models designed to recognize paralinguistic information, particularly speech emotion and style, are difficult to train because of the limited labeled datasets available. In this work, we present a…
Emotions modulate speech acoustics as well as language. The latter influences the sequences of phonemes that are produced, which in turn further modulate the acoustics. Therefore, phonemes impact emotion recognition in two ways: (1) they introduce an additional source of variability in speech signal…
Emotion recognition algorithms rely on data annotated with high quality labels. However, emotion expression and perception are inherently subjective. There is generally not a single annotation that can be unambiguously declared "correct." As a result, annotations are colored by the manner in which t…
DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadra…
This work focuses on the use of acoustic cues for modeling turn-taking in dyadic spoken dialogues. Previous work has shown that speaker intentions (e.g., asking a question, uttering a backchannel, etc.) can influence turn-taking behavior and are good predictors of turn-transitions in spoken dialogue…
In this paper, we show that convolutional neural networks can be directly applied to temporal low-level acoustic features to identify emotionally salient regions without the need for defining or applying utterance-level statistics. We show how a convolutional neural network can be applied to minimal…
Emotion is expressed over both speech and song. Previous works have found that although spoken and sung emotion recognition are different tasks, they are related. Classifiers that explicitly utilize this relatedness can achieve better performance than classifiers that do not. Further, research in sp…
Speech contains patterns that can be altered by the mood of an individual. There is an increasing focus on automated and distributed methods to collect and monitor speech from large groups of patients suffering from mental health disorders. However, as the scope of these collections increases, the v…