Speech emotion recognition has become increasingly important in recent years
due to its potential applications in healthcare, customer service, and
personalization of dialogue systems. However, a major issue in this field is
the lack of datasets that adequately represent basic emotional states across
various language families. As datasets covering Slavic languages are rare,
there is a need to address this research gap. This paper presents the
development of nEMO, a novel corpus of emotional speech in Polish. The dataset
comprises over 3 hours of samples recorded with the participation of nine
actors portraying six emotional states: anger, fear, happiness, sadness,
surprise, and a neutral state. The text material used was carefully selected to
represent the phonetics of the Polish language adequately. The corpus is freely
available under the terms of a Creative Commons license (CC BY-NC-SA 4.0).

该研究介绍了 nEMO，一个包含波兰语情感语音的新型语料库，它由参演的九名演员录制的三个多小时的样本组成，描绘了六种情感状态：愤怒、恐惧、快乐、悲伤、惊讶和中性状态。

nEMO：波兰情感语音数据集

nEMO: Dataset of Emotional Speech in Polish

The human voice effectively communicates a range of emotions with nuanced
variations in acoustics. Existing emotional speech corpora are limited in that
they are either (a) highly curated to induce specific emotions with predefined
categories that may not capture the full extent of emotional experiences, or
(b) entangled in their semantic and prosodic cues, limiting the ability to
study these cues separately. To overcome this challenge, we propose a new
approach called 'Genetic Algorithm with People' (GAP), which integrates human
decision and production into a genetic algorithm. In our design, we allow
creators and raters to jointly optimize the emotional prosody over generations.
We demonstrate that GAP can efficiently sample from the emotional speech space
and capture a broad range of emotions, and show comparable results to
state-of-the-art emotional speech corpora. GAP is language-independent and
supports large crowd-sourcing, thus can support future large-scale
cross-cultural research.

提出一种名为 “Genetic Algorithm with People”（GAP）的方法，该方法集成了人类的决策和生产过程，以有效地采样情绪语音空间并捕捉广泛的情绪范围，支持大规模跨文化研究。

缩小韵律差距：遗传算法结合人类有效采样情感韵律

Bridging the prosody GAP: Genetic Algorithm with People to efficiently sample emotional prosody

In this paper, we present a database of emotional speech intended to be
open-sourced and used for synthesis and generation purpose. It contains data
for male and female actors in English and a male actor in French. The database
covers 5 emotion classes so it could be suitable to build synthesis and voice
transformation systems with the potential to control the emotional dimension in
a continuous way. We show the data's efficiency by building a simple MLP system
converting neutral to angry speech style and evaluate it via a CMOS perception
test. Even though the system is a very simple one, the test show the efficiency
of the data which is promising for future work.

本文介绍了一种情感语音数据库，涵盖男性和女性以及法语男性等不同语音。该数据库可以用于合成和生成语音，并且可以控制情感维度。通过建立一个简单的 MLP 系统，我们可以将中性语音转换为愤怒语音风格，并通过 CMOS 感知测试评估其效率，结果表明该系统的效率非常高，这为未来工作打下了良好的基础。