In this paper the task of emotion recognition from speech is considered.
Proposed approach uses deep recurrent neural network trained on a sequence of
acoustic features calculated over small speech intervals. At the same time
special probabilistic-nature CTC loss function allows to consider long
utterances containing both emotional and neutral parts. The effectiveness of
such an approach is shown in two ways. Firstly, the comparison with recent
advances in this field is carried out. Secondly, human performance on the same
task is measured. Both criteria show the high quality of the proposed method.

本文提出了一种利用深度递归神经网络来识别语音情感的方法，通过对小语音时间间隔内计算的一系列声学特征进行训练，同时采用特殊的概率性 CTC 损失函数来考虑包含情感和中性部分的长语音片段，在与最近进展的比较和人类表现的对比中展示了该方法的高质量。

用循环神经网络从语音中识别情感

Emotion Recognition From Speech With Recurrent Neural Networks

There has been a lot of prior work on representation learning for speech
recognition applications, but not much emphasis has been given to an
investigation of effective representations of affect from speech, where the
paralinguistic elements of speech are separated out from the verbal content. In
this paper, we explore denoising autoencoders for learning paralinguistic
attributes i.e. categorical and dimensional affective traits from speech. We
show that the representations learnt by the bottleneck layer of the autoencoder
are highly discriminative of activation intensity and at separating out
negative valence (sadness and anger) from positive valence (happiness). We
experiment with different input speech features (such as FFT and log-mel
spectrograms with temporal context windows), and different autoencoder
architectures (such as stacked and deep autoencoders). We also learn utterance
specific representations by a combination of denoising autoencoders and BLSTM
based recurrent autoencoders. Emotion classification is performed with the
learnt temporal/dynamic representations to evaluate the quality of the
representations. Experiments on a well-established real-life speech dataset
(IEMOCAP) show that the learnt representations are comparable to state of the
art feature extractors (such as voice quality features and MFCCs) and are
competitive with state-of-the-art approaches at emotion and dimensional affect
recognition.

本文探讨运用去噪自编码器来学习从语音中分离出言语内容以外的语调特征，实现从语音中学习情感的判别，包括基于时间窗口的快速傅里叶变换和梅尔频率倒谱系数等不同的输入方法和不同的自编码器结构，结果显示学习到的表示具备与优秀的特征提取器相当的能力。

从语音中学习情感表现

Learning Representations of Affect from Speech

We have developed a sparse mathematical representation of speech that
minimizes the number of active model neurons needed to represent typical speech
sounds. The model learns several well-known acoustic features of speech such as
harmonic stacks, formants, onsets and terminations, but we also find more
exotic structures in the spectrogram representation of sound such as localized
checkerboard patterns and frequency-modulated excitatory subregions flanked by
suppressive sidebands. Moreover, several of these novel features resemble
neuronal receptive fields reported in the Inferior Colliculus (IC), as well as
auditory thalamus and cortex, and our model neurons exhibit the same tradeoff
in spectrotemporal resolution as has been observed in IC. To our knowledge,
this is the first demonstration that receptive fields of neurons in the
ascending mammalian auditory pathway beyond the auditory nerve can be predicted
based on coding principles and the statistical properties of recorded sounds.

本研究开发了一种稀疏的语音数学表示法，该方法使典型语音声音所需的活跃模型神经元数量最小化，其中包括感受野的检查板图案和侧抑制侧波带。此外，我们还发现了几个声谱图结构，其新颖特征类似于指示升高哺乳动物听觉通路中感受野的功能。