Speech Emotion Recognition (SER) has emerged as a critical component of the
next generation human-machine interfacing technologies. In this work, we
propose a new dual-level model that predicts emotions based on both MFCC
features and mel-spectrograms produced from raw audio signals. Each utterance
is preprocessed into MFCC features and two mel-spectrograms at different
time-frequency resolutions. A standard LSTM processes the MFCC features, while
a novel LSTM architecture, denoted as Dual-Sequence LSTM (DS-LSTM), processes
the two mel-spectrograms simultaneously. The outputs are later averaged to
produce a final classification of the utterance. Our proposed model achieves,
on average, a weighted accuracy of 72.7% and an unweighted accuracy of
73.3%---a 6% improvement over current state-of-the-art unimodal models---and is
comparable with multimodal models that leverage textual information as well as
audio signals.

提出了一种新的双层模型，通过使用来自原始音频信号的 MFCC 特征和 mel-spectrograms 预测情感，其中一个标准 LSTM 处理 MFCC 特征，而一个新的双序列 LSTM 架构同时处理两个 mel-spectrograms，最终将输出平均，实现了在情感识别中的 6% 的提高。

使用双序列 LSTM 架构的语音情感识别

Speech Emotion Recognition with Dual-Sequence LSTM Architecture

We present a system for covert automated deception detection in real-life
courtroom trial videos. We study the importance of different modalities like
vision, audio and text for this task. On the vision side, our system uses
classifiers trained on low level video features which predict human
micro-expressions. We show that predictions of high-level micro-expressions can
be used as features for deception prediction. Surprisingly, IDT (Improved Dense
Trajectory) features which have been widely used for action recognition, are
also very good at predicting deception in videos. We fuse the score of
classifiers trained on IDT features and high-level micro-expressions to improve
performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio
domain also provide a significant boost in performance, while information from
transcripts is not very beneficial for our system. Using various classifiers,
our automated system obtains an AUC of 0.877 (10-fold cross-validation) when
evaluated on subjects which were not part of the training set. Even though
state-of-the-art methods use human annotations of micro-expressions for
deception detection, our fully automated approach outperforms them by 5%. When
combined with human annotations of micro-expressions, our AUC improves to
0.922. We also present results of a user-study to analyze how well do average
humans perform on this task, what modalities they use for deception detection
and how they perform if only one modality is accessible. Our project page can
be found at https://doubaibai.github.io/DARE/.

提出一种针对真实法庭审判视频中隐蔽自动欺诈检测的系统，该系统利用从图像、声音和文本三个层面得到的低级和高级特征预测人类微表情，其中，IDT 特征 和 MFCC 特征也对系统性能提供了重要帮助，系统在经过交叉验证的数据集上的 AUC 为 0.877，优于现有接近最优的方法，同时也提供了一个用户研究，分析一般人在这个任务上的表现。