654 research outputs found
Speech Emotion Recognition Using Multi-hop Attention Mechanism
In this paper, we are interested in exploiting textual and acoustic data of
an utterance for the speech emotion classification task. The baseline approach
models the information from audio and text independently using two deep neural
networks (DNNs). The outputs from both the DNNs are then fused for
classification. As opposed to using knowledge from both the modalities
separately, we propose a framework to exploit acoustic information in tandem
with lexical data. The proposed framework uses two bi-directional long
short-term memory (BLSTM) for obtaining hidden representations of the
utterance. Furthermore, we propose an attention mechanism, referred to as the
multi-hop, which is trained to automatically infer the correlation between the
modalities. The multi-hop attention first computes the relevant segments of the
textual data corresponding to the audio signal. The relevant textual data is
then applied to attend parts of the audio signal. To evaluate the performance
of the proposed system, experiments are performed in the IEMOCAP dataset.
Experimental results show that the proposed technique outperforms the
state-of-the-art system by 6.5% relative improvement in terms of weighted
accuracy.Comment: 5 pages, Accepted as a conference paper at ICASSP 2019 (oral
presentation
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition
Speech emotion recognition is a challenging task in speech processing field.
For this reason, feature extraction process has a crucial importance to
demonstrate and process the speech signals. In this work, we represent a model,
which feeds raw audio files directly into the deep neural networks without any
feature extraction stage for the recognition of emotions utilizing six
different data sets, EMO-DB, RAVDESS, TESS, CREMA, SAVEE, and TESS+RAVDESS. To
demonstrate the contribution of proposed model, the performance of traditional
feature extraction techniques namely, mel-scale spectogram, mel-frequency
cepstral coefficients, are blended with machine learning algorithms, ensemble
learning methods, deep and hybrid deep learning techniques. Support vector
machine, decision tree, naive Bayes, random forests models are evaluated as
machine learning algorithms while majority voting and stacking methods are
assessed as ensemble learning techniques. Moreover, convolutional neural
networks, long short-term memory networks, and hybrid CNN- LSTM model are
evaluated as deep learning techniques and compared with machine learning and
ensemble learning methods. To demonstrate the effectiveness of proposed model,
the comparison with state-of-the-art studies are carried out. Based on the
experiment results, CNN model excels existent approaches with 95.86% of
accuracy for TESS+RAVDESS data set using raw audio files, thence determining
the new state-of-the-art. The proposed model performs 90.34% of accuracy for
EMO-DB with CNN model, 90.42% of accuracy for RAVDESS with CNN model, 99.48% of
accuracy for TESS with LSTM model, 69.72% of accuracy for CREMA with CNN model,
85.76% of accuracy for SAVEE with CNN model in speaker-independent audio
categorization problems.Comment: 14 pages, 6 Figures, 8 Table
BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data
This paper presents a novel BigEAR big data framework that employs
psychological audio processing chain (PAPC) to process smartphone-based
acoustic big data collected when the user performs social conversations in
naturalistic scenarios. The overarching goal of BigEAR is to identify moods of
the wearer from various activities such as laughing, singing, crying, arguing,
and sighing. These annotations are based on ground truth relevant for
psychologists who intend to monitor/infer the social context of individuals
coping with breast cancer. We pursued a case study on couples coping with
breast cancer to know how the conversations affect emotional and social well
being. In the state-of-the-art methods, psychologists and their team have to
hear the audio recordings for making these inferences by subjective evaluations
that not only are time-consuming and costly, but also demand manual data coding
for thousands of audio files. The BigEAR framework automates the audio
analysis. We computed the accuracy of BigEAR with respect to the ground truth
obtained from a human rater. Our approach yielded overall average accuracy of
88.76% on real-world data from couples coping with breast cancer.Comment: 6 pages, 10 equations, 1 Table, 5 Figures, IEEE International
Workshop on Big Data Analytics for Smart and Connected Health 2016, June 27,
2016, Washington DC, US
Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines
The problem of inferring human emotional state automatically from speech has become one of the central problems in Man Machine Interaction (MMI). Though Support Vector Machines (SVMs) were used in several worksfor emotion recognition from speech, the potential of using probabilistic SVMs for this task is not explored. The emphasis of the current work is on how to use probabilistic SVMs for the efficient recognition of emotions from speech. Emotional speech corpuses for two Dravidian languages- Telugu & Tamil- were constructed for assessing the recognition accuracy of Probabilistic SVMs. Recognition accuracy of the proposed model is analyzed using both Telugu and Tamil emotional speech corpuses and compared with three of the existing works. Experimental results indicated that the proposed model is significantly better compared with the existing methods
Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review
Death by suicide is the seventh leading death cause worldwide. The recent
advancement in Artificial Intelligence (AI), specifically AI applications in
image and voice processing, has created a promising opportunity to
revolutionize suicide risk assessment. Subsequently, we have witnessed
fast-growing literature of research that applies AI to extract audiovisual
non-verbal cues for mental illness assessment. However, the majority of the
recent works focus on depression, despite the evident difference between
depression symptoms and suicidal behavior and non-verbal cues. This paper
reviews recent works that study suicide ideation and suicide behavior detection
through audiovisual feature analysis, mainly suicidal voice/speech acoustic
features analysis and suicidal visual cues. Automatic suicide assessment is a
promising research direction that is still in the early stages. Accordingly,
there is a lack of large datasets that can be used to train machine learning
and deep learning models proven to be effective in other, similar tasks.Comment: Manuscript submitted to Arificial Intelligence Reviews (2022
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