BackgroundTo evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early-stage Alzheimer's disease (AD).MethodsHealthy elderly control (HC) subjects and patients with MCI or AD were recorded while performing several short cognitive vocal tasks. The voice recordings were processed, and the first vocal markers were extracted using speech signal processing techniques. Second, the vocal markers were tested to assess their “power” to distinguish among HC, MCI, and AD. The second step included training automatic classifiers for detecting MCI and AD, using machine learning methods and testing the detection accuracy.ResultsThe classification accuracy of automatic audio analyses were as follows: between HCs and those with MCI, 79% ± 5%; between HCs and those with AD, 87% ± 3%; and between those with MCI and those with AD, 80% ± 5%, demonstrating its assessment utility.ConclusionAutomatic speech analyses could be an additional objective assessment tool for elderly with cognitive decline.
We present a new implementation of emotion recognition from the para-lingual information in the speech, based on a deep neural network, applied directly to spectrograms. This new method achieves higher recognition accuracy compared to previously published results, while also limiting the latency. It processes the speech input in smaller segments-up to 3 seconds, and splits a longer input into non-overlapping parts to reduce the prediction latency. The deep network comprises common neural network toolsconvolutional and recurrent networks-which are shown to effectively learn the information that represents emotions directly from spectrograms. Convolution-only lowercomplexity deep network achieves a prediction accuracy of 66% over four emotions (tested on IEMOCAP-a common evaluation corpus), while a combined convolution-LSTM higher-complexity model achieves 68%. The use of spectrograms in the role of speech-representing features enables effective handling of background non-speech signals such as music (excl. singing) and crowd noise, even at noise levels comparable with the speech signal levels. Using harmonic modeling to remove non-speech components from the spectrogram, we demonstrate significant improvement of the emotion recognition accuracy in the presence of unknown background non-speech signals.
Our results indicate the potential value of vocal analytics and the use of a mobile application for accurate automatic differentiation between SCI, MCI and AD. This tool can provide the clinician with meaningful information for assessment and monitoring of people with MCI and AD based on a non-invasive, simple and low-cost method.
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