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date: 17 October 2019

Abstract and Keywords

Machines are able to obtain rich information from the human voice with a certain reliability. This can comprise information about the affective or mental state, but also traits of the speaker. This chapter introduces all the different technical steps needed in such intelligent voice analysis. Typically, the first step involves extraction of meaningful acoustic features, which are then transformed into a suitable representation. The acoustic information can be augmented by linguistic features originating from a speech-to-text transcription. The features are finally decoded on different levels using machine-learning methods. Recently, ‘deep learning’ has received growing interest, where deep artificial neural networks are used to decode the information. From this, end-to-end learning has evolved, where even the feature extraction step is learned seamlessly, through to the decoding step, mimicking the recognition process in the human brain. Subsequent to the description of according and further frequently encountered methods, the chapter concludes with some future perspective.

Keywords: acoustic feature, audio word, bag-of-words, machine learning, hidden Markov model, artificial neural network, deep learning, end-to-end learning, automatic speech recognition, computational paralinguistics

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