Silent Computational Paralinguistics (SCP) -the assessment of speaker states and traits from non-audibly spoken communication -has rarely been targeted in the rich body of either Computational Paralinguistics or Silent Speech Processing. Here, we provide first steps towards this challenging but potentially highly rewarding endeavour: Paralinguistics can enrich spoken language interfaces, while Silent Speech Processing enables confidential and unobtrusive spoken communication for everybody, including mute speakers. We approach SCP by using speech-related biosignals stemming from facial muscle activities captured by surface electromyography (EMG). To demonstrate the feasibility of SCP, we select one speaker trait (speaker identity) and one speaker state (speaking mode). We introduce two promising strategies for SCP: (1) deriving paralinguistic speaker information directly from EMG of silently produced speech versus (2) first converting EMG into an audible speech signal followed by conventional computational paralinguistic methods. We compare traditional feature extraction and decision making approaches to more recent deep representation and transfer learning by convolutional and recurrent neural networks, using openly available EMG data. We find that paralinguistics can be assessed not only from acoustic speech but also from silent speech captured by EMG.
Electromyographic (EMG) signals recorded during speech production encode information on articulatory muscle activity and also on the facial expression of emotion, thus representing a speech-related biosignal with strong potential for paralinguistic applications. In this work, we estimate the electrical activity of the muscles responsible for speech articulation directly from the speech signal. To this end, we first perform a neural conversion of speech features into electromyographic time domain features, and then attempt to retrieve the original EMG signal from the time domain features. We propose a feed forward neural network to address the first step of the problem (speech features to EMG features) and a neural network composed of a convolutional block and a bidirectional long short-term memory block to address the second problem (true EMG features to EMG signal). We observe that four out of the five originally proposed time domain features can be estimated reasonably well from the speech signal. Further, the five time domain features are able to predict the original speech-related EMG signal with a concordance correlation coefficient of 0.663. We further compare our results with the ones achieved on the inverse problem of generating acoustic speech features from EMG features.
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