RESUMEN
In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.
Asunto(s)
Algoritmos , Dispositivos Electrónicos Vestibles , Humanos , Procesamiento de Señales Asistido por Computador , Localización de SonidosRESUMEN
In early stages, patients with bipolar disorder are often diagnosed as having unipolar depression in mood disorder diagnosis. Because the long-term monitoring is limited by the delayed detection of mood disorder, an accurate and one-time diagnosis is desirable to avoid delay in appropriate treatment due to misdiagnosis. In this paper, an elicitation-based approach is proposed for realizing a one-time diagnosis by using responses elicited from patients by having them watch six emotion-eliciting videos. After watching each video clip, the conversations, including patient facial expressions and speech responses, between the participant and the clinician conducting the interview were recorded. Next, the hierarchical spectral clustering algorithm was employed to adapt the facial expression and speech response features by using the extended Cohn-Kanade and eNTERFACE databases. A denoizing autoencoder was further applied to extract the bottleneck features of the adapted data. Then, the facial and speech bottleneck features were input into support vector machines to obtain speech emotion profiles (EPs) and the modulation spectrum (MS) of the facial action unit sequence for each elicited response. Finally, a cell-coupled long short-term memory (LSTM) network with an L -skip fusion mechanism was proposed to model the temporal information of all elicited responses and to loosely fuse the EPs and the MS for conducting mood disorder detection. The experimental results revealed that the cell-coupled LSTM with the L -skip fusion mechanism has promising advantages and efficacy for mood disorder detection.