170 research outputs found
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition
Alongside acoustic information, linguistic features based on speech
transcripts have been proven useful in Speech Emotion Recognition (SER).
However, due to the scarcity of emotion labelled data and the difficulty of
recognizing emotional speech, it is hard to obtain reliable linguistic features
and models in this research area. In this paper, we propose to fuse Automatic
Speech Recognition (ASR) outputs into the pipeline for joint training SER. The
relationship between ASR and SER is understudied, and it is unclear what and
how ASR features benefit SER. By examining various ASR outputs and fusion
methods, our experiments show that in joint ASR-SER training, incorporating
both ASR hidden and text output using a hierarchical co-attention fusion
approach improves the SER performance the most. On the IEMOCAP corpus, our
approach achieves 63.4% weighted accuracy, which is close to the baseline
results achieved by combining ground-truth transcripts. In addition, we also
present novel word error rate analysis on IEMOCAP and layer-difference analysis
of the Wav2vec 2.0 model to better understand the relationship between ASR and
SER.Comment: Accepted for ICASSP 202
Recognizing emotions in spoken dialogue with acoustic and lexical cues
Automatic emotion recognition has long been a focus of Affective Computing. It has
become increasingly apparent that awareness of human emotions in Human-Computer
Interaction (HCI) is crucial for advancing related technologies, such as dialogue
systems. However, performance of current automatic emotion recognition is
disappointing compared to human performance. Current research on emotion
recognition in spoken dialogue focuses on identifying better feature representations
and recognition models from a data-driven point of view. The goal of this thesis
is to explore how incorporating prior knowledge of human emotion recognition
in the automatic model can improve state-of-the-art performance of automatic
emotion recognition in spoken dialogue. Specifically, we study this by proposing
knowledge-inspired features representing occurrences of disfluency and non-verbal
vocalisation in speech, and by building a multimodal recognition model that combines
acoustic and lexical features in a knowledge-inspired hierarchical structure. In our
study, emotions are represented with the Arousal, Expectancy, Power, and Valence
emotion dimensions. We build unimodal and multimodal emotion recognition
models to study the proposed features and modelling approach, and perform emotion
recognition on both spontaneous and acted dialogue.
Psycholinguistic studies have suggested that DISfluency and Non-verbal
Vocalisation (DIS-NV) in dialogue is related to emotions. However, these affective
cues in spoken dialogue are overlooked by current automatic emotion recognition
research. Thus, we propose features for recognizing emotions in spoken dialogue
which describe five types of DIS-NV in utterances, namely filled pause, filler, stutter,
laughter, and audible breath. Our experiments show that this small set of features
is predictive of emotions. Our DIS-NV features achieve better performance than
benchmark acoustic and lexical features for recognizing all emotion dimensions in
spontaneous dialogue. Consistent with Psycholinguistic studies, the DIS-NV features
are especially predictive of the Expectancy dimension of emotion, which relates to
speaker uncertainty. Our study illustrates the relationship between DIS-NVs and
emotions in dialogue, which contributes to Psycholinguistic understanding of them
as well. Note that our DIS-NV features are based on manual annotations, yet our
long-term goal is to apply our emotion recognition model to HCI systems. Thus, we
conduct preliminary experiments on automatic detection of DIS-NVs, and on using
automatically detected DIS-NV features for emotion recognition. Our results show
that DIS-NVs can be automatically detected from speech with stable accuracy, and
auto-detected DIS-NV features remain predictive of emotions in spontaneous dialogue.
This suggests that our emotion recognition model can be applied to a fully automatic
system in the future, and holds the potential to improve the quality of emotional
interaction in current HCI systems.
To study the robustness of the DIS-NV features, we conduct cross-corpora
experiments on both spontaneous and acted dialogue. We identify how dialogue
type influences the performance of DIS-NV features and emotion recognition models.
DIS-NVs contain additional information beyond acoustic characteristics or lexical
contents. Thus, we study the gain of modality fusion for emotion recognition with the
DIS-NV features. Previous work combines different feature sets by fusing modalities
at the same level using two types of fusion strategies: Feature-Level (FL) fusion,
which concatenates feature sets before recognition; and Decision-Level (DL) fusion,
which makes the final decision based on outputs of all unimodal models. However,
features from different modalities may describe data at different time scales or levels
of abstraction. Moreover, Cognitive Science research indicates that when perceiving
emotions, humans make use of information from different modalities at different
cognitive levels and time steps. Therefore, we propose a HierarchicaL (HL) fusion
strategy for multimodal emotion recognition, which incorporates features that describe
data at a longer time interval or which are more abstract at higher levels of its
knowledge-inspired hierarchy. Compared to FL and DL fusion, HL fusion incorporates
both inter- and intra-modality differences. Our experiments show that HL fusion
consistently outperforms FL and DL fusion on multimodal emotion recognition in both
spontaneous and acted dialogue. The HL model combining our DIS-NV features with
benchmark acoustic and lexical features improves current performance of multimodal
emotion recognition in spoken dialogue.
To study how other emotion-related tasks of spoken dialogue can benefit from the
proposed approaches, we apply the DIS-NV features and the HL fusion strategy to
recognize movie-induced emotions. Our experiments show that although designed
for recognizing emotions in spoken dialogue, DIS-NV features and HL fusion
remain effective for recognizing movie-induced emotions. This suggests that other
emotion-related tasks can also benefit from the proposed features and model structure
A review of natural language processing in contact centre automation
Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco
Recognizing Induced Emotions of Movie Audiences From Multimodal Information
Recognizing emotional reactions of movie audiences to affective movie content is a challenging task in affective computing. Previous research on induced emotion recognition has mainly focused on using audio-visual movie content. Nevertheless, the relationship between the perceptions of the affective movie content (perceived emotions) and the emotions evoked in the audiences (induced emotions) is unexplored. In this work, we studied the relationship between perceived and induced emotions of movie audiences. Moreover, we investigated multimodal modelling approaches to predict movie induced emotions from movie content based features, as well as physiological and behavioral reactions of movie audiences. To carry out analysis of induced and perceived emotions, we first extended an existing database for movie affect analysis by annotating perceived emotions in a crowd-sourced manner. We find that perceived and induced emotions are not always consistent with each other. In addition, we show that perceived emotions, movie dialogues, and aesthetic highlights are discriminative for movie induced emotion recognition besides spectators’ physiological and behavioral reactions. Also, our experiments revealed that induced emotion recognition could benefit from including temporal information and performing multimodal fusion. Moreover, our work deeply investigated the gap between affective content analysis and induced emotion recognition by gaining insight into the relationships between aesthetic highlights, induced emotions, and perceived emotions
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