IPR is a python tool to work with intonation. It is derived from my PhD research.
- Abstract from the dissertation
This model provides experimental evidence for the validity of an intonational phonology. The widely used Autosegmental-Metrical theory contends that the phonological structure of intonation can be expressed with two tonal targets (L/H tones and derivatives) and retrieved from its phonetic implementations. However, it has not been specifically demonstrated so far in a systematic way. This dissertation argues that this view on intonational phonology considers the phonetic forms of intonation as instances of phonologically structured intonational units forming functionally discrete categories (tones and derivatives).
The model of Pattern Recognition for Intonation (PRInt) applies the concepts of categorization (vagueness, prototype, degrees of typicality) to intonation in order to abstract the phonological structure of intonational categories from the ranking, by degree of typicality, of their variations in phonetic implementation.
First, instances belonging to an intonation category are collected. Second, a pattern recognition module, relying on the 4-layer structure protocol, extracts a feature vector from the phonetic data of each instance: a sequence of structurally organized tones (L/H tones and derivatives).
Third, a fuzzy classifier, using two functions (frequency and similarity), organizes the data from the feature vectors of all instances by degree of typicality (grade of membership of values in multisets) and generates the phonological structure of the intonation category, the prototypical pattern, extracted from all instances, and that subsumes them all. It also recreates the phonetic implementations of the phonological structure but with their features ranked by degree of typicality. This allows the model to distinguish phonologically distinct structures from phonetic variations of the same phonological structure.
The model successfully extracted the phonological intonation structure associated to three modalities of closed questions in French: neutral, doubtful, and surprised. It found that neutral and doubtful closed questions are phonologically distinct while surprise is a phonetic allocontour of the neutral modality, in line with prior characterizations of these patterns. It demonstrated that a bi-tonal phonological structure of intonation can be retrieved from phonetic variations.
A versatile modeling tool, PRInt will be developed to use its acquired knowledge to evaluate the categorical status of novel instances and to extract multiple phonological units from mixed corpora.
more to come...