134 research outputs found
Multimodal Speech Emotion Recognition Using Audio and Text
Speech emotion recognition is a challenging task, and extensive reliance has
been placed on models that use audio features in building well-performing
classifiers. In this paper, we propose a novel deep dual recurrent encoder
model that utilizes text data and audio signals simultaneously to obtain a
better understanding of speech data. As emotional dialogue is composed of sound
and spoken content, our model encodes the information from audio and text
sequences using dual recurrent neural networks (RNNs) and then combines the
information from these sources to predict the emotion class. This architecture
analyzes speech data from the signal level to the language level, and it thus
utilizes the information within the data more comprehensively than models that
focus on audio features. Extensive experiments are conducted to investigate the
efficacy and properties of the proposed model. Our proposed model outperforms
previous state-of-the-art methods in assigning data to one of four emotion
categories (i.e., angry, happy, sad and neutral) when the model is applied to
the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.Comment: 7 pages, Accepted as a conference paper at IEEE SLT 201
Comparative Studies of Detecting Abusive Language on Twitter
The context-dependent nature of online aggression makes annotating large
collections of data extremely difficult. Previously studied datasets in abusive
language detection have been insufficient in size to efficiently train deep
learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much
greater in size and reliability, has been released. However, this dataset has
not been comprehensively studied to its potential. In this paper, we conduct
the first comparative study of various learning models on Hate and Abusive
Speech on Twitter, and discuss the possibility of using additional features and
context data for improvements. Experimental results show that bidirectional GRU
networks trained on word-level features, with Latent Topic Clustering modules,
is the most accurate model scoring 0.805 F1.Comment: ALW2: 2nd Workshop on Abusive Language Online to be held at EMNLP
2018 (Brussels, Belgium), October 31st, 201
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
In this paper, we propose a novel end-to-end neural architecture for ranking
candidate answers, that adapts a hierarchical recurrent neural network and a
latent topic clustering module. With our proposed model, a text is encoded to a
vector representation from an word-level to a chunk-level to effectively
capture the entire meaning. In particular, by adapting the hierarchical
structure, our model shows very small performance degradations in longer text
comprehension while other state-of-the-art recurrent neural network models
suffer from it. Additionally, the latent topic clustering module extracts
semantic information from target samples. This clustering module is useful for
any text related tasks by allowing each data sample to find its nearest topic
cluster, thus helping the neural network model analyze the entire data. We
evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic
domain question answering dataset, which is related to Samsung products. The
proposed model shows state-of-the-art results for ranking question-answer
pairs.Comment: 10 pages, Accepted as a conference paper at NAACL 201
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
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
While deep learning techniques have shown promising results in many natural
language processing (NLP) tasks, it has not been widely applied to the clinical
domain. The lack of large datasets and the pervasive use of domain-specific
language (i.e. abbreviations and acronyms) in the clinical domain causes slower
progress in NLP tasks than that of the general NLP tasks. To fill this gap, we
employ word/subword-level based models that adopt large-scale data-driven
methods such as pre-trained language models and transfer learning in analyzing
text for the clinical domain. Empirical results demonstrate the superiority of
the proposed methods by achieving 90.6% accuracy in medical domain natural
language inference task. Furthermore, we inspect the independent strengths of
the proposed approaches in quantitative and qualitative manners. This analysis
will help researchers to select necessary components in building models for the
medical domain.Comment: 9 pages, Accepted to ACL 2019 workshop on BioNL
Blank Collapse: Compressing CTC emission for the faster decoding
Connectionist Temporal Classification (CTC) model is a very efficient method
for modeling sequences, especially for speech data. In order to use CTC model
as an Automatic Speech Recognition (ASR) task, the beam search decoding with an
external language model like n-gram LM is necessary to obtain reasonable
results. In this paper we analyze the blank label in CTC beam search deeply and
propose a very simple method to reduce the amount of calculation resulting in
faster beam search decoding speed. With this method, we can get up to 78%
faster decoding speed than ordinary beam search decoding with a very small loss
of accuracy in LibriSpeech datasets. We prove this method is effective not only
practically by experiments but also theoretically by mathematical reasoning. We
also observe that this reduction is more obvious if the accuracy of the model
is higher.Comment: Accepted in Interspeech 202
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