Adaptive lexical processing of semantic competitors extends to alternative names: Evidence from blocked-cyclic picture naming

Naming a picture (e.g., "duck") in the context of semantically related pictures (e.g., "eagle," "stork," "parrot") takes longer than naming it in the context of unrelated pictures (e.g., "knave," "toast," "atlas"). Adaptive models of word production attribute this semantic interference effect in blocked-cyclic naming (BCN) to an adaptive mechanism that makes competitor words, (e.g., the semantically related word "eagle" for the target word "duck") which are activated but not selected for production, less accessible for future retrieval. Results from a recent picture-word-interference study, however, suggested that alternative names (e.g., "bird" for "duck") might be exempt from this mechanism, challenging adaptive lexical processing as a general mechanism. We tested whether converging evidence is obtained in BCN. In Experiment 1, we embedded pictures responded to with alternative (category) names (e.g., "bird") into contexts composed of pictures responded to with specific (exemplar) names (e.g., "duck," "eagle," "stork," and "parrot"). If alternative names are exempt from adaptive lexical processing, interference in the homogeneous context should be found for specific name items but not for alternative name items. In contrast to this prediction, there was similar-sized interference for both types of items. In Experiment 2, we replaced the alternative name items with unrelated items. For these items, interference was largely diminished, ruling out that the effect found in Experiment 1 is a general set effect. Overall, our data suggest that alternative names are not special with respect to adaptive lexical processing.

Speaker Counting Based on a Novel Hive Shaped Nested Microphone Array by WPT and 2D Adaptive SRP Algorithms in Near-Field Scenarios

Speech processing algorithms, especially sound source localization (SSL), speech enhancement, and speaker tracking are considered to be the main fields in this application. Most speech processing algorithms require knowing the number of speakers for real implementation. In this article, a novel method for estimating the number of speakers is proposed based on the hive shaped nested microphone array (HNMA) by wavelet packet transform (WPT) and 2D sub-band adaptive steered response power (SB-2DASRP) with phase transform (PHAT) and maximum likelihood (ML) filters, and, finally, the agglomerative classification and elbow criteria for obtaining the number of speakers in near-field scenarios. The proposed HNMA is presented for aliasing and imaging elimination and preparing the proper signals for the speaker counting method. In the following, the Blackman-Tukey spectral estimation method is selected for detecting the proper frequency components of the recorded signal. The WPT is considered for smart sub-band processing by focusing on the frequency bins of the speech signal. In addition, the SRP method is implemented in 2D format and adaptively by ML and PHAT filters on the sub-band signals. The SB-2DASRP peak positions are extracted on various time frames based on the standard deviation (SD) criteria, and the final number of speakers is estimated by unsupervised agglomerative clustering and elbow criteria. The proposed HNMA-SB-2DASRP method is compared with the frequency-domain magnitude squared coherence (FD-MSC), i-vector probabilistic linear discriminant analysis (i-vector PLDA), ambisonics features of the correlational recurrent neural network (AF-CRNN), and speaker counting by density-based classification and clustering decision (SC-DCCD) algorithms on noisy and reverberant environments, which represents the superiority of the proposed method for real implementation.

Adaptive Feature Extraction for Blood Vessel Segmentation and Contrast Recalculation in Laser Speckle Contrast Imaging

Microvasculature analysis in biomedical images is essential in the medical area to evaluate diseases by extracting properties of blood vessels, such as relative blood flow or morphological measurements such as diameter. Given the advantages of Laser Speckle Contrast Imaging (LSCI), several studies have aimed to reduce inherent noise to distinguish between tissue and blood vessels at higher depths. These studies have shown that computing Contrast Images (CIs) with Analysis Windows (AWs) larger than standard sizes obtains better statistical estimators. The main issue is that larger samples combine pixels of microvasculature with tissue regions, reducing the spatial resolution of the CI. This work proposes using adaptive AWs of variable size and shape to calculate the features required to train a segmentation model that discriminates between blood vessels and tissue in LSCI. The obtained results show that it is possible to improve segmentation rates of blood vessels up to 45% in high depths (≈900 μm) by extracting features adaptively. The main contribution of this work is the experimentation with LSCI images under different depths and exposure times through adaptive processing methods, furthering the understanding the performance of the different approaches under these conditions. Results also suggest that it is possible to train a segmentation model to discriminate between pixels belonging to blood vessels and those belonging to tissue. Therefore, an adaptive feature extraction method may improve the quality of the features and thus increase the classification rates of blood vessels in LSCI.