Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

The anatomy of urban social networks and its implications in the searchability problem

The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure.

Social stress and the reactivation of latent herpes simplex virus type 1

Psychological stress is thought to contribute to reactivation of latent herpes simplex virus (HSV). Although several animal models have been developed in an effort to reproduce different pathogenic aspects of HSV keratitis or labialis, until now, no good animal model existed in which application of a psychological laboratory stressor results in reliable reactivation of the virus. Reported herein, disruption of the social hierarchy within colonies of mice increased aggression among cohorts, activated the hypothalamic-pituitary-adrenal axis, and caused reactivation of latent HSV type 1 in greater than 40% of latently infected animals. However, activation of the hypothalamic-pituitary-adrenal axis using restraint stress did not activate the latent virus. Thus, the use of social stress in mice provides a good model in which to investigate the neuroendocrine mechanisms that underlie behaviorally mediated reactivation of latent herpesviruses.

TravelBot: Journey Disruption Alerts Utilising Social Media and Linked Data

Postprint

Distinct Actions and Cooperative Roles of ROCK and mDia in Rho Small G Protein-induced Reorganization of the Actin Cytoskeleton in Madin–Darby Canine Kidney Cells

Rho, a member of the Rho small G protein family, regulates the formation of stress fibers and focal adhesions in various types of cultured cells. We investigated here the actions of ROCK and mDia, both of which have been identified to be putative downstream target molecules of Rho, in Madin–Darby canine kidney cells. The dominant active mutant of RhoA induced the formation of parallel stress fibers and focal adhesions, whereas the dominant active mutant of ROCK induced the formation of stellate stress fibers and focal adhesions, and the dominant active mutant of mDia induced the weak formation of parallel stress fibers without affecting the formation of focal adhesions. In the presence of C3 ADP-ribosyltransferase for Rho, the dominant active mutant of ROCK induced the formation of stellate stress fibers and focal adhesions, whereas the dominant active mutant of mDia induced only the diffuse localization of actin filaments. These results indicate that ROCK and mDia show distinct actions in reorganization of the actin cytoskeleton. The dominant negative mutant of either ROCK or mDia inhibited the formation of stress fibers and focal adhesions, indicating that both ROCK and mDia are necessary for the formation of stress fibers and focal adhesions. Moreover, inactivation and reactivation of both ROCK and mDia were necessary for the 12-O-tetradecanoylphorbol-13-acetate–induced disassembly and reassembly, respectively, of stress fibers and focal adhesions. The morphologies of stress fibers and focal adhesions in the cells expressing both the dominant active mutants of ROCK and mDia were not identical to those induced by the dominant active mutant of Rho. These results indicate that at least ROCK and mDia cooperatively act as downstream target molecules of Rho in the Rho-induced reorganization of the actin cytoskeleton.

Differences in antigen recognition and cytolytic activity of CD8+ and CD8− T cells that express the same antigen-specific receptor

CD8+ and CD8− T cell lines expressing the same antigen-specific receptor [the 2C T cell receptor (TCR)] were compared for ability to bind soluble peptide-MHC and to lyse target cells. The 2C TCR on CD8− cells bound a syngeneic MHC (Kb+)-peptide complex 10–100 times less well than the same TCR on CD8+ cells, and the CD8− 2C cells lysed target cells presenting this complex very poorly. Surprisingly, however, the CD8− cells differed little from CD8+ cells in ability to bind an allogeneic MHC (Ld+)-peptide complex and to lyse target cells presenting this complex. The CD8+/CD8− difference provided an opportunity to estimate how long TCR engagements with peptide-MHC have to persist to initiate the cytolytic T cell response.

Image recognition: Visual grouping, recognition, and learning

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.

Distinct functions and cooperative interaction of the subunits of the transporter associated with antigen processing (TAP)

The ATP-binding cassette (ABC) transporter TAP translocates peptides from the cytosol to awaiting MHC class I molecules in the endoplasmic reticulum. TAP is made up of the TAP1 and TAP2 polypeptides, which each possess a nucleotide binding domain (NBD). However, the role of ATP in peptide binding and translocation is poorly understood. We present biochemical and functional evidence that the NBDs of TAP1 and TAP2 are non-equivalent. Photolabeling experiments with 8-azido-ATP demonstrate a cooperative interaction between the two NBDs that can be stimulated by peptide. The substitution of key lysine residues in the Walker A motifs of TAP1 and TAP2 suggests that TAP1-mediated ATP hydrolysis is not essential for peptide translocation but that TAP2-mediated ATP hydrolysis is critical, not only for translocation, but for peptide binding.

Involvement of Asn-293 in stereospecific agonist recognition and in activation of the beta 2-adrenergic receptor.

To investigate the molecular mechanism for stereospecific binding of agonists to beta 2-adrenergic receptors we used receptor models to identify potential binding sites for the beta-OH-group of the ligand, which defines the chiral center. Ser-165, located in transmembrane helix IV, and Asn-293, situated in the upper half of transmembrane helix VI, were identified as potential binding sites. Mutation of Ser-165 to Ala did not change the binding of either isoproterenol isomer as revealed after transient expression in human embryonic kidney (HEK)-293 cells. In contrast, a receptor mutant in which Asn-293 was replaced by Leu showed substantial loss of stereospecific isoproterenol binding. Adenylyl cyclase stimulation by this mutant after stable expression in CHO cells confirmed the substantial loss of stereospecificity for isoproterenol. In a series of agonists the loss of affinity in the Leu-293 mutant receptor was strongly correlated with the intrinsic activity of the compounds. Full agonists showed a 10-30-fold affinity loss, whereas partial agonists had almost the same affinity for both receptors. Stereospecific recognition of antagonists was unaltered in the Leu-293 mutant receptor. These data indicate a relationship between stereospecificity and intrinsic activity of agonists and suggest that Asn-293 is important for both properties of the agonist-receptor interaction.

Self-recognition and abstraction abilities in the common chimpanzee studied with distorting mirrors.

The reactions of chimpanzees to regular mirrors and the results of the standard Gallup mark test have been well documented. In addition to using the mark test to demonstrate self-recognition in a regular mirror, we exposed six female chimpanzees to mirrors that produced distorted or multiplied self-images. Their reactions to their self-images, in terms of mirror-guided self-referenced behaviors, indicated that correct assessment of the source of the mirror image was made by each subject in each of the mirrors. Recognition of a distorted self-image implies an ability for abstraction in the subjects in that the distortion must be rationalized before self-recognition occurs. The implications of these results in terms of illuminating the relative importance of feature and contingency of movement cues to self-recognition are discussed.

Cue recognition and cue elaboration in learning from examples.

This paper describes the processes used by students to learn from worked-out examples and by working through problems. Evidence is derived from protocols of students learning secondary school mathematics and physics. The students acquired knowledge from the examples in the form of productions (condition-->action): first discovering conditions under which the actions are appropriate and then elaborating the conditions to enhance efficiency. Students devoted most of their attention to the condition side of the productions. Subsequently, they generalized the productions for broader application and acquired specialized productions for special problem classes.

D1 cap region involved in the receptor recognition and neural cell survival activity of human ciliary neurotrophic factor.

Human ciliary neurotrophic factor (hCNTF), which promotes the cell survival and differentiation of motor and other neurons, is a protein belonging structurally to the alpha-helical cytokine family. hCNTF was subjected to three-dimensional structure modeling and site-directed mutagenesis to analyze its structure-function relationship. The replacement of Lys-155 with any other amino acid residue resulted in abolishment of neural cell survival activity, and some of the Glu-153 mutant proteins had 5- to 10-fold higher biological activity. The D1 cap region (around the boundary between the CD loop and helix D) of hCNTF, including both Glu-153 and Lys-155, was shown to play a key role in the biological activity of hCNTF as one of the putative receptor-recognition sites. In this article, the D1 cap region of the 4-helix-bundle proteins is proposed to be important in receptor recognition and biological activity common to alpha-helical cytokine proteins reactive with gp130, a component protein of the receptors.

New multivalent calixarene-based ligands for protein recognition and inhibition

One of the challenges that concerns chemistry is the design of molecules able to modulate protein-protein and protein-ligand interactions, since these are involved in many physiological and pathological processes. The interactions occurring between proteins and their natural counterparts can take place through reciprocal recognition of rather large surface areas, through recognition of single contact points and single residues, through inclusion of the substrates in specific, more or less deep binding sites. In many cases, the design of synthetic molecules able to interfere with the processes involving proteins can benefit from the possibility of exploiting the multivalent effect. Multivalency, widely spread in Nature, consists in the simultaneous formation between two entities (cell-cell, cell-protein, protein-protein) of multiple equivalent ligand-recognition site complexes. In this way the whole interaction results particularly strong and specific. Calixarenes furnish a very interesting scaffold for the preparation of multivalent ligands and in the last years calixarene-based ligands demonstrated their remarkable capability to recognize and inhibit or restore the activity of different proteins, with a high efficiency and selectivity in several recognition phenomena. The relevance and versatility of these ligands is due to the different exposition geometries of the binding units that can be explored exploiting the conformational properties of these macrocycles, the wide variety of functionalities that can be linked to their structure at different distances from the aromatic units and to their intrinsic multivalent nature. With the aim of creating new multivalent systems for protein targeting, the work reported in this thesis regards the synthesis and properties of glycocalix[n]arenes and guanidino calix[4]arenes for different purposes. Firstly, a new bolaamphiphile glycocalix[4]arene in 1,3-alternate geometry, bearing cellobiose, was synthesized for the preparation of targeted drug delivery systems based on liposomes. The formed stable mixed liposomes obtained by mixing the macrocycle with DOPC were shown to be able of exploiting the sugar units emerging from the lipid bilayer to agglutinate Concanavalin A, a lectin specific for glucose. Moreover, always thanks to the presence of the glycocalixarene in the layer, the same liposomes demonstrated through preliminary experiments to be uptaken by cancer cells overexpressing glucose receptors on their exterior surface more efficiently respect to simple DOPC liposomes lacking glucose units in their structure. Then a small library of glycocalix[n]arenes having different valency and geometry was prepared, for the creation of potentially active immunostimulants against Streptococcus pneumoniae, particularly the 19F serotype, one of the most virulent. These synthesized glycocalixarenes bearing β-N-acetylmannosamine as antigenic unit were compared with the natural polysaccharide on the binding to the specific anti-19F human polyclonal antibody, to verify their inhibition potency. Among all, the glycocalixarene based on the conformationally mobile calix[4]arene resulted the more efficient ligand, probably due its major possibility to explore the antibody surface and dispose the antigenic units in a proper arrangement for the interaction process. These results pointed out the importance of how the different multivalent presentation in space of the glycosyl units can influence the recognition phenomena. At last, NMR studies, using particularly 1H-15N HSQC experiments, were performed on selected glycocalix[6]arenes and guanidino calix[4]arenes blocked in the cone geometry, in order to better understand protein-ligand interactions. The glycosylated compounds were studied with Ralstonia solanacearum lectin, in order to better understand the nature of the carbohydrate‐lectin interactions in solution. The series of cationic calixarene was employed with three different acidic proteins: GB1, Fld and alpha synuclein. Particularly GB1 and Fld were observed to interact with all five cationic calix[4]arenes but showing different behaviours and affinities.