¿Qué es la fonología computacional?

Computational phonology is not one thing. Rather, it is an umbrella term which may refer to work on formal language theory, computer-implemented models of cognitive processes, and corpus methods derived from the literature on natural language processing (NLP). This article gives an overview of these distinct areas, identifying commonalities and differences in the goals of each area, as well as highlighting recent results of interest. The overview is necessarily brief and subjective. Broadly speaking, it is argued that learning is a pervasive theme in these areas, but the core questions and concerns vary too much to define a coherent field. Computational phonologists are more united by a shared body of formal knowledge than they are by a shared sense of what the important questions are.La fonología computacional no representa un campo unitario, sino que es un término genérico que puede hacer referencia a obras sobre teorías de lenguajes formales; a modelos de procesos cognitivos implementados por ordenador; y a métodos de trabajo con corpus, derivados de la bibliografía sobre procesamiento del lenguaje natural (PLN). Este artículo ofrece una visión de conjunto de estas distintas áreas, identifica los puntos comunes y las diferencias en los objetivos de cada una, y pone de relieve algunos de los últimos resultados más relevantes. Esta visión de conjunto es necesariamente breve y subjetiva. En términos generales, se argumenta que el aprendizaje es un tema recurrente en estos ámbitos, pero las preguntas y los problemas centrales varían demasiado como para definir un área de estudio unitaria y coherente. Los fonólogos computacionales están unidos por un cúmulo común de conocimientos formales más que por un parecer compartido acerca de cuáles son las preguntas importantes

Intelligent Assistant Language Understanding On Device

It has recently become feasible to run personal digital assistants on phones and other personal devices. In this paper we describe a design for a natural language understanding system that runs on device. In comparison to a server-based assistant, this system is more private, more reliable, faster, more expressive, and more accurate. We describe what led to key choices about architecture and technologies. For example, some approaches in the dialog systems literature are difficult to maintain over time in a deployment setting. We hope that sharing learnings from our practical experiences may help inform future work in the research community

An orthographic effect in loanword adaptation

Loanword corpora have been an important tool in studying the relationship between speech perception and native-language phonotactics. Recent work has challenged this use of loanword corpora on methodological grounds, based on the fact that source and possibly loan orthography conditions the adaptation. The present study replicates and extends this finding by using information theory to quantify the relative strength of orthographic effects, in the adaptation of English vowels into Korean. It is found that the orthographic effect is strong for unstressed vowels, but almost unnoticable for stressed vowels. It is proposed that orthography plays a large role in adaptation only when the source form is perceptually compatible with multiple phonological parses in the borrowing language

A Method for Projecting Features from Observed Sets of Phonological Classes

Given a set of phonological features, we can enumerate a set of phonological classes. Here we consider the inverse of this problem: given a set of phonological classes, can we derive a feature system? We show that this is indeed possible, using a collection of algorithms that assign features to a set of input classes and differ in terms of what types of features are permissible. This work bears on theories of both language-specific and universal features, provides testable predictions of the featurizations available to learners, and serves as a useful component in computational models of feature learning

A Method for Projecting Features from Observed Sets of Phonological Classes

Given a set of phonological features, we can enumerate a set of phonological classes. Here we consider the inverse of this problem: given a set of phonological classes, can we derive a feature system? We show that this is indeed possible, using a collection of algorithms that assign features to a set of input classes and differ in terms of what types of features are permissible. This work bears on theories of both language-specific and universal features, provides testable predictions of the featurizations available to learners, and serves as a useful component in computational models of feature learning

Similarity in the generalization of implicitly learned sound patterns

Abstract: It is likely that generalization of implicitly learned sound patterns to novel words and sounds is structured by a similarity metric, but how may this metric best be captured? We report on an experiment where participants were exposed to an artificial phonology, and frequency ratings were used to probe implicit abstraction of onset statistics. Non-words bearing an onset that was presented during initial exposure were subsequently rated most frequent, indicating that participants generalized onset statistics to new non-words. Participants also rated non-words with untrained onsets as somewhat frequent, indicating generalization to onsets that had not been used during the exposure phase. While generalization could be accounted for in terms of featural distance, it was insensitive to natural class structure. Generalization to untrained sounds was predicted better by models requiring prior linguistic knowledge (either traditional distinctive features or articulatory phonetic information) than by a model based on a linguistically naïve measure of acoustic similarity

Toward a generative theory of language transfer: Experiment and modeling of sC prothesis in L2 Spanish

When native Spanish speakers produce English words with initial [s]-consonant clusters (sC), they sometimes produce a prothetic vowel, e.g. stigma > estigma. This paper reports a production experiment on this phenomena, as well as computational modelling of the experimental results. Carlisle (1991a) proposed the ‘resyllabification account’ in which prothesis is a language transfer effect, whose essential motivation is to satisfy L1/Spanish syllable phonotactics. Replicating all previous work, a greater rate of prothesis was found in postconsonantal contexts than in postvocalic contexts (Rick (e)stinks > Ricky (e)stinks). A novel prediction is that when prothesis occurs, the [s] should have durational characteristics associated with the coda position, whereas it should have onset characteristics when prothesis does not occur; this was found. Another prediction is that a grammar which captures the variability in prothesis should in some sense be “between” the L1/Spanish and L2/English grammars. This latter prediction was tested by developing a constraint-based analysis of sC prothesis in Maximum Entropy Harmonic Grammar (Goldwater & Johnson, 2003). The results were consistent with a view of language transfer as ‘linear interpolation’ of constraint weights, conditioned on an ‘effort’ constraint reflecting how phonological planning varies with task/ modality demands