Emotion detection from voice recordings

语音信号携带多个级别的信息。从语音记录中，我们可以提取发音的单词、说话人的身份、口语甚至说话人的情感状态。在过去的五年里，基于不同人类形式的情感识别领域的兴趣大大增加，如语音、心率等。建立一个健壮的情感检测系统在医疗和电信等领域非常有用。在医学领域，情绪检测一般可以被认为是诊断和跟踪抑郁症患者的重要工具。通过声音识别一个人的情绪状态为开发自动对话系统开辟了新的视角，该系统能够每天与家里的患者交流，甚至一天几次，为医生提供报告。尽管已经做了很多研究，但实际的情感识别系统的性能仍然不足以满足这些现实生活中的应用。大多数情感检测系统的设计都侧重于语音建模，而不是特征提取方面。通常，特征向量由经典倒谱特征(如MFCC)和一些韵律特征(如语音语调)的组合组成。文献中提出的有限的结果使我们认为，我们应该专注于提高用于训练情感识别系统的特征的质量，而不是专注于建模方面。为此，我们的研究将集中在从语音信号中提取更准确的特征。为了达到这一目标，我们将建立三个主轴。第一个目标是致力于DNN体系结构的开发，该体系结构能够直接从原始语音信号学习分类功能。第二个步骤包括开发另一种能够学习归一化功能的DNN体系结构，以便能够混合由来自不同记录条件的语音信号组成的不同数据库。最后，在第三把斧头中，我们将专注于从说话者所说的单词中提取的高级特征，如音素持续时间和其他证据。在此研究期间所做的改进将对构建可靠的医疗应用程序非常实用。事实上，这类应用程序将帮助卫生专业人员提高加拿大的治疗质量，从而对他们有用。此外，我们的发现还可以应用于语音技术的其他几个领域。提出的方法将有助于开发命令和控制应用程序，而无需使用复杂和耗时/耗电的语音识别引擎。这将帮助加拿大的初创企业或小企业在其应用程序中添加基于语音的控制，而无需聘请语音识别专家。

The speech signal carries several levels of information. From a voice recording, we can extract pronounced words, the speaker's identity, the spoken language or even the speaker's emotional state.In the last five years, there has been a great increase of interest in the field of emotion recognition based on different human modalities, such as speech, heart rate, etc. Building a robust emotion detection system can be very useful in several areas such as medicine and telecommunications. In the medical field, detecting emotions in general can be considered an important tool for diagnosing and following patients suffering from depression. The identification of the emotional state of a person from his voice opens new perspectives for the development of an automated dialogue system, one capable of communicating with patients at home daily and even several times a day to produce a report for the physician.Although much research has been done, actual emotion recognition systems performances are still not adequate for these real life applications. The majority of emotion detection systems have been designed by focussing on the speech modeling rather than the feature extraction aspect. Usually, feature vectors are made of a combination of classical cepstral features (such as MFCC) augmented with some prosodic characteristics (such as speech intonation). The modest results presented in the literature make us think that we should focus on improving the quality of features used for training emotion identification systems instead of focussing on the modeling aspect. For this reason, our research will focus on extracting more accurate features from the speech signal.Three main axes will be established in order to reach this objective. The first axe consists of working on the development of a DNN architecture capable of learning the classification function directly from the raw speech signal. The second axe consists of developing another DNN architecture capable of learning a normalization function in order to be able to mix different databases made of speech signals from different recording conditions. Finally, in the third axe, we will concentrate on high-level features such as phoneme durations and other evidence extracted from words said by the speaker.Improvements made during this research will be very practical for building reliable healthcare applications. Indeed, such applications would be useful for health professionals by helping them improve the quality of treatments in Canada. Moreover, our findings could be applied to several other fields of speech technologies. The approach presented would facilitate the development of command and control applications without the use of a complex and time/power consuming speech recognition engine. This would help start-ups or small businesses in Canada to add voice-based control to their applications without needing to hire a speech recognition specialist.