■需要有效利用处于痴呆症各个阶段的人的情绪进行预防,早期干预,和护理规划。有了可用于理解和解决人们情感需求的技术,这项研究旨在开发语音情绪识别(SER)技术,以对痴呆症高危人群的情绪进行分类。
■通过人类听觉评估,将痴呆症高危人群的语音样本分为不同的情绪,对其结果进行了注释,以指导深度学习方法。该架构结合了卷积神经网络,长期短期记忆,注意层,和Wav2Vec2,一种新颖的特征提取器,用于开发自动语音情感识别。
■在参与者的演讲中发现了27种情绪。这些情绪分为6种详细的情绪:幸福,兴趣,悲伤,挫败感,愤怒,中立,并进一步进入三种基本情绪:积极的,负,中立。为了提高算法性能,使用不同的数据源-语音和文本-以及不同的情绪数量,应用了多种学习方法。最终,一个2阶段的算法-初始基于文本的分类,然后是基于语音的分析-实现了最高的准确性,达到70%。
■本研究中确定的不同情绪归因于参与者的特征和数据收集的方法。痴呆症高危人群对伴侣机器人的讲话也解释了SER算法相对较低的性能。因此,这项研究表明,系统全面地构建了一个来自痴呆症患者的数据集。
UNASSIGNED: The emotions of people at various stages of dementia need to be effectively utilized for prevention, early intervention, and care planning. With technology available for understanding and addressing the emotional needs of people, this study aims to develop speech emotion recognition (SER) technology to classify emotions for people at high risk of dementia.
UNASSIGNED: Speech samples from people at high risk of dementia were categorized into distinct emotions via human auditory assessment, the outcomes of which were annotated for guided deep-learning method. The architecture incorporated convolutional neural network, long short-term memory, attention layers, and Wav2Vec2, a novel feature extractor to develop automated speech-emotion recognition.
UNASSIGNED: Twenty-seven kinds of Emotions were found in the speech of the participants. These emotions were grouped into 6 detailed emotions: happiness, interest, sadness, frustration, anger, and neutrality, and further into 3 basic emotions: positive, negative, and neutral. To improve algorithmic performance, multiple learning approaches were applied using different data sources-voice and text-and varying the number of emotions. Ultimately, a 2-stage algorithm-initial text-based classification followed by voice-based analysis-achieved the highest accuracy, reaching 70%.
UNASSIGNED: The diverse emotions identified in this study were attributed to the characteristics of the participants and the method of data collection. The speech of people at high risk of dementia to companion robots also explains the relatively low performance of the SER algorithm. Accordingly, this study suggests the systematic and comprehensive construction of a dataset from people with dementia.