Phonology and intonation

The encoding standards for phonology and intonation are designed to facilitate consistent annotation of the phonological and intonational aspects of information structure, in languages across a range ofprosodic types. The guidelines are designed with the aim that a nonspecialist in phonology can both implement and interpret the resulting annotation

Prosodic description: An introduction for fieldworkers

This article provides an introductory tutorial on prosodic features such as tone and accent for researchers working on little-known languages. It specifically addresses the needs of non-specialists and thus does not presuppose knowledge of the phonetics and phonology of prosodic features. Instead, it intends to introduce the uninitiated reader to a field often shied away from because of its (in part real, but in part also just imagined) complexities. It consists of a concise overview of the basic phonetic phenomena (section 2) and the major categories and problems of their functional and phonological analysis (sections 3 and 4). Section 5 gives practical advice for documenting and analyzing prosodic features in the field.National Foreign Language Resource Cente

Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

Mandarin Chinese is characterized by being a tonal language; the pitch (or $F_0$) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase which must be accounted for in any analysis which attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase and duration is presented which combines elements from Functional Data Analysis, Compositional Data Analysis and Linear Mixed Effects Models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and non-linguistic covariates. The model is applied to the COSPRO-1 data set, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50 thousand phonetically diverse sample $F_0$ contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation.Comment: 49 pages, 13 figures, small changes to discussio

Measuring Syntactic Complexity in Spoken and Written Learner Language: Comparing the Incomparable?

Spoken and written language are two modes of language. When learners aim at higher skill levels, the expected outcome of successful second language learning is usually to become a fluent speaker and writer who can produce accurate and complex language in the target language. There is an axiomatic difference between speech and writing, but together they form the essential parts of learners’ L2 skills. The two modes have their own characteristics, and there are differences between native and nonnative language use. For instance, hesitations and pauses are not visible in the end result of the writing process, but they are characteristic of nonnative spoken language use. The present study is based on the analysis of L2 English spoken and written productions of 18 L1 Finnish learners with focus on syntactic complexity. As earlier spoken language segmentation units mostly come from fluency studies, we conducted an experiment with a new unit, the U-unit, and examined how using this unit as the basis of spoken language segmentation affects the results. According to the analysis, written language was more complex than spoken language. However, the difference in the level of complexity was greatest when the traditional units, T-units and AS-units, were used in segmenting the data. Using the U-unit revealed that spoken language may, in fact, be closer to written language in its syntactic complexity than earlier studies had suggested. Therefore, further research is needed to discover whether the differences in spoken and written learner language are primarily due to the nature of these modes or, rather, to the units and measures used in the analysis

Information structural notions and the fallacy of invariant correlates

In a first step, definitions of the irreducible information structural categories are given, and in a second step, it is shown that there are no invariant phonological or otherwise grammatical correlates of these categories. In other words, the phonology, syntax or morphology are unable to define information structure. It is a common mistake that information structural categories are expressed by invariant grammatical correlates, be they syntactic, morphological or phonological. It is rather the case that grammatical cues help speaker and hearer to sort out which element carries which information structural role, and only in this sense are the grammatical correlates of information structure important. Languages display variation as to the role of grammar in enhancing categories of information structure, and this variation reflects the variation found in the ‘normal’ syntax and phonology of languages

Exploiting Contextual Information for Prosodic Event Detection Using Auto-Context

Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information to train new classifiers. By iteratively using updated probabilities as the contextual information, the algorithm can accurately model contextual dependencies and improve classification ability. The advantages of this method include its flexible structure and the ability of capturing contextual relationships. When using the auto-context algorithm based on support vector machine, we can improve the detection accuracy by about 3% and F-score by more than 7% on both two-way and four-way pitch accent detections in combination with the acoustic context. For boundary detection, the accuracy improvement is about 1% and the F-score improvement reaches 12%. The new algorithm outperforms conditional random fields, especially on boundary detection in terms of F-score. It also outperforms an n-gram language model on the task of pitch accent detection