Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies

Many studies of speech perception assess the intelligibility of spoken sentence stimuli by means of transcription tasks (‘type out what you hear’). The intelligibility of a given stimulus is then often expressed in terms of percentage of words correctly reported from the target sentence. Yet scoring the participants’ raw responses for words correctly identified from the target sentence is a time- consuming task, and hence resource-intensive. Moreover, there is no consensus among speech scientists about what specific protocol to use for the human scoring, limiting the reliability of human scores. The present paper evaluates various forms of fuzzy string matching between participants’ responses and target sentences, as automated metrics of listener transcript accuracy. We demonstrate that one particular metric, the Token Sort Ratio, is a consistent, highly efficient, and accurate metric for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores (best correlation: r = 0.940) and a strong relationship to acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for assessment of listener transcript accuracy in large-scale speech intelligibility studies. See https://tokensortratio.netlify.app for an online implementation

Comparative Reconstruction Probabilistically: The Role of Inventory and Phonotactics

I introduce a novel quantitative methodology for evaluating manual comparative reconstructions. This method is incumbent on the existence of a manual comparative reconstruction and, unlike previous quantitative methods, cannot give a result contradictory to the reconstruction. The primary goal for this framework is to reconcile traditional and quantitative methodologies and act as an objective and accessible platform for comparative reconstruction, thereby extending the scope of historical linguistics further into the past. A few theoretical corollaries of the framework are also presented. It is shown that the likelihood that a reconstruction is spurious is related to some of the phonological properties of the descendent language. This likelihood is inversely correlated with mean word-length and segmental inventory size. Additionally, most active phonological processes and cooccurrence restrictions in the language – such as phonotactic constraints, prosodic effects, segment harmony, and neutralization – all serve to increase the likelihood that a reconstruction to that language is spurious

Linguistic probes into human history

Dit proefschrift omvat vijf reeds gepubliceerde artikelen en een studie die binnenkort verschijnt. Daarin heb ik taalkundige methoden onderzocht, getoetst en gebruikt om linguïstische variëteiten te classificeren op basis van steekproeven die bestaan uit lexicale items.De gerapporteerde studies hebben betrekking op de classificatie van Nederlandse variëteiten uit Nederland, talen en dialecten uit Spanje, Bantu-variëteiten uit Gabon, Tanzania en tenslotte Turkse en Indo-Iraanse talen die gesproken worden in Kirgizstan, Tadzjikistan en Oezbekistan.Binnen een multidisciplinair perspectief dat gericht is op het verschaffen van een hoger niveau van antropologische synthese wordt de taalkundige diversiteit gebruikt als proxy voor de culturele verschillen van de overeenkomstige populaties en wordt vervolgens vergeleken met de variabiliteit van familienamen (hun aantal, frequentie en geografische verdeling) of met genetische verschillen die gebaseerd zijn op moleculaire kenmerken in het DNA.Met betrekking tot dat laatste kan de analyse van familienamen migraties zichtbaar maken die mogelijk in historische tijden hebben plaatsgevonden, en kunnen we regio's onderscheiden die veel immigranten hebben ontvangen die wegtrokken uit demografisch stabieler gebleven regio's. Wij vermoeden dat dergelijke migratiepatronen dialect- en taalcontact hebben beïnvloed. Dit is een nieuw perspectief van waaruit we de effecten van migratie op taalverandering kunnen onderzoeken.This thesis in linguistics includes five published articles and one study to appear, in which I review, test and use computational linguistic methods to classify languages and dialects consisting of lexical items – the sort of material that is generally readily available from linguistic atlases and databases. To compare linguistic varieties and classify them, methods that lead to the computation of a linguistic distance matrix are used.The studies reported respectively concern the classification of Dutch dialects from the Netherlands; languages and dialects from Spain; Bantu languages from Gabon, Tanzania and, finally, Turkic and Indo-Iranian languages spoken in Kyrgyzstan, Tajikistan and Uzbekistan.In a multidisciplinary perspective aimed at providing a higher level of anthropological synthesis, linguistic diversity is used as a proxy for the cultural differences of corresponding populations and is then compared to the variability of family names (their number, frequency and geographic distribution) or to genetic differences based on molecular markers on the DNA. The analysis of family names enables the depiction of migrations which have taken place in historical times, and, allows us to distinguish regions that have received many immigrants from those that have remained demographically more stable. We conjecture that such migration patterns have influenced dialect and language contact. This is a novel perspective from which we may examine the effects of migration on language change, for example it appears that Spanish languages have remained lively because the regions where they are spoken have often be quite isolated demographically

Multi-faceted Assessment of Trademark Similarity

Trademarks are intellectual property assets with potentially high reputational value. Their infringement may lead to lost revenue, lower profits and damages to brand reputation. A test normally conducted to check whether a trademark is highly likely to infringe other existing, already registered, trademarks is called a likelihood of confusion test. One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1,400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting. Keywords—Trademark assessment, trademark infringement, trademark retrieval, degree of similarity, fuzzy aggregation, semantic similarity, phonetic similarity, visual similarity

A computational model of the relationship between speech intelligibility and speech acoustics

abstract: Speech intelligibility measures how much a speaker can be understood by a listener. Traditional measures of intelligibility, such as word accuracy, are not sufficient to reveal the reasons of intelligibility degradation. This dissertation investigates the underlying sources of intelligibility degradations from both perspectives of the speaker and the listener. Segmental phoneme errors and suprasegmental lexical boundary errors are developed to reveal the perceptual strategies of the listener. A comprehensive set of automated acoustic measures are developed to quantify variations in the acoustic signal from three perceptual aspects, including articulation, prosody, and vocal quality. The developed measures have been validated on a dysarthric speech dataset with various severity degrees. Multiple regression analysis is employed to show the developed measures could predict perceptual ratings reliably. The relationship between the acoustic measures and the listening errors is investigated to show the interaction between speech production and perception. The hypothesize is that the segmental phoneme errors are mainly caused by the imprecise articulation, while the sprasegmental lexical boundary errors are due to the unreliable phonemic information as well as the abnormal rhythm and prosody patterns. To test the hypothesis, within-speaker variations are simulated in different speaking modes. Significant changes have been detected in both the acoustic signals and the listening errors. Results of the regression analysis support the hypothesis by showing that changes in the articulation-related acoustic features are important in predicting changes in listening phoneme errors, while changes in both of the articulation- and prosody-related features are important in predicting changes in lexical boundary errors. Moreover, significant correlation has been achieved in the cross-validation experiment, which indicates that it is possible to predict intelligibility variations from acoustic signal.Dissertation/ThesisDoctoral Dissertation Speech and Hearing Science 201

Studying Evolutionary Change: Transdisciplinary Advances in Understanding and Measuring Evolution

Evolutionary processes can be found in almost any historical, i.e. evolving, system that erroneously copies from the past. Well studied examples do not only originate in evolutionary biology but also in historical linguistics. Yet an approach that would bind together studies of such evolving systems is still elusive. This thesis is an attempt to narrowing down this gap to some extend. An evolving system can be described using characters that identify their changing features. While the problem of a proper choice of characters is beyond the scope of this thesis and remains in the hands of experts we concern ourselves with some theoretical as well data driven approaches. Having a well chosen set of characters describing a system of different entities such as homologous genes, i.e. genes of same origin in different species, we can build a phylogenetic tree. Consider the special case of gene clusters containing paralogous genes, i.e. genes of same origin within a species usually located closely, such as the well known HOX cluster. These are formed by step- wise duplication of its members, often involving unequal crossing over forming hybrid genes. Gene conversion and possibly other mechanisms of concerted evolution further obfuscate phylogenetic relationships. Hence, it is very difficult or even impossible to disentangle the detailed history of gene duplications in gene clusters. Expanding gene clusters that use unequal crossing over as proposed by Walter Gehring leads to distinctive patterns of genetic distances. We show that this special class of distances helps in extracting phylogenetic information from the data still. Disregarding genome rearrangements, we find that the shortest Hamiltonian path then coincides with the ordering of paralogous genes in a cluster. This observation can be used to detect ancient genomic rearrangements of gene clus- ters and to distinguish gene clusters whose evolution was dominated by unequal crossing over within genes from those that expanded through other mechanisms. While the evolution of DNA or protein sequences is well studied and can be formally described, we find that this does not hold for other systems such as language evolution. This is due to a lack of detectable mechanisms that drive the evolutionary processes in other fields. Hence, it is hard to quantify distances between entities, e.g. languages, and therefore the characters describing them. Starting out with distortions of distances, we first see that poor choices of the distance measure can lead to incorrect phylogenies. Given that phylogenetic inference requires additive metrics we can infer the correct phylogeny from a distance matrix D if there is a monotonic, subadditive function ζ such that ζ^−1(D) is additive. We compute the metric-preserving transformation ζ as the solution of an optimization problem. This result shows that the problem of phylogeny reconstruction is well defined even if a detailed mechanistic model of the evolutionary process is missing. Yet, this does not hinder studies of language evolution using automated tools. As the amount of available and large digital corpora increased so did the possibilities to study them automatically. The obvious parallels between historical linguistics and phylogenetics lead to many studies adapting bioinformatics tools to fit linguistics means. Here, we use jAlign to calculate bigram alignments, i.e. an alignment algorithm that operates with regard to adjacency of letters. Its performance is tested in different cognate recognition tasks. Using pairwise alignments one major obstacle is the systematic errors they make such as underestimation of gaps and their misplacement. Applying multiple sequence alignments instead of a pairwise algorithm implicitly includes more evolutionary information and thus can overcome the problem of correct gap placement. They can be seen as a generalization of the string-to-string edit problem to more than two strings. With the steady increase in computational power, exact, dynamic programming solutions have become feasible in practice also for 3- and 4-way alignments. For the pairwise (2-way) case, there is a clear distinction between local and global alignments. As more sequences are consid- ered, this distinction, which can in fact be made independently for both ends of each sequence, gives rise to a rich set of partially local alignment problems. So far these have remained largely unexplored. Thus, a general formal frame- work that gives raise to a classification of partially local alignment problems is introduced. It leads to a generic scheme that guides the principled design of exact dynamic programming solutions for particular partially local alignment problems