UH Biocomputation Group - Sentiment Analysis and Text Mininghttp://biocomputation.herts.ac.uk/2020-01-29T11:03:39+00:00Towards Discriminative representation learning for speech Emotion recognition2020-01-29T11:03:39+00:002020-01-29T11:03:39+00:00Emil Dmitruktag:biocomputation.herts.ac.uk,2020-01-29:/2020/01/29/towards-discriminative-representation-learning-for-speech-emotion-recognition.html<p class="first last">Yi Sun's journal club session where he will talk about the paper "Towards Discriminative representation learning for speech Emotion recognition".</p>
<p>This week on Journal Club session Yi Sun will talk about the paper "Towards Discriminative representation learning for speech Emotion recognition".</p>
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<p>In intelligent speech interaction, automatic speech emotion recognition (SER)
plays an important role in understanding user intention. While sentimental
speech has different speaker characteristics but similar acoustic attributes,
one vital challenge in SER is how to learn robust and discriminative
representations for emotion inferring. In this paper, inspired by human
emotion perception, we propose a novel representation learning component (RLC)
for SER system, which is constructed with Multi-head Self-attention and Global
Context-aware Attention Long Short-Term Memory Recurrent Neutral Network
(GCA-LSTM). With the ability of Multi-head Self-attention mechanism in
modeling the element-wise correlative dependencies, RLC can exploit the
common patterns of sentimental speech features to enhance emotion-salient
information importing in representation learning. By employing GCA-LSTM,
RLC can selectively focus on emotion-salient factors with the consideration
of entire utterance context, and gradually produce discriminative representation
for emotion inferring. Experiments on public emotional benchmark database
IEMOCAP and a tremendous realistic interaction database demonstrate the
outperformance of the proposed SER framework, with 6.6% to 26.7% relative
improvement on unweighted accuracy compared to state-of-the-art techniques.</p>
<p>Papers:</p>
<ul class="simple">
<li>Runnan Li et al. (2019) <a class="reference external" href="https://www.ijcai.org/Proceedings/2019/703">"Towards Discriminative Representation Learning for Speech Emotion Recognition"</a> ,
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligenced, Main track, Pages 5060-5066</li>
</ul>
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<p><strong>Date:</strong> 31/01/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: B200</p>