Correlation between phonetic factors and linguistic events regarding a prosodic pattern of European Portuguese: a practical proposal

In this article a prosodic model for European Portuguese (henceforth EP) based on a linguistic approach is described. It was developed in the scope of the Antigona Project, an electronic-commerce system using a speech interface (Speech to Text plus Text To Speech, the latter based on a time concatenation technique) for EP language. The purpose of our work is to contribute with practical strategies in order to improve synthetic speech quality and naturalness, concerning prosodic processing. It is also our goal to show that syntactic structures strongly determine prosody patterns in EP. It is also important to emphasize the pragmatic commercial objective of this system, which is selling a product. Therefore, this type of application deals with a specific vocabulary choice, it is displayed in predictable syntactic constructions and sentences, making prosodic contours and focus become expected. This study was held in intimate articulation between the engineering experience and tools and the linguistic approach. We believe that this work represents an important achievement for future research on synthetic speech processing in particular for EP. Moreover, it can be applied to other Romanic languages, regarding their syntactic resemblances

A computational model of prosody for Yorùbá text-to-speech synthesis

This work examines prosody modelling for the Standard Yorùbá (SY) language in the context of computer text-to-speech synthesis applications. The thesis of this research is that it is possible to develop a practical prosody model by using appropriate computational tools and techniques which combines acoustic data with an encoding of the phonological and phonetic knowledge provided by experts. Our prosody model is conceptualised around a modular holistic framework. The framework is implemented using the Relational Tree (R-Tree) techniques (Ehrich and Foith, 1976). R-Tree is a sophisticated data structure that provides a multi-dimensional description of a waveform. A Skeletal Tree (S-Tree) is first generated using algorithms based on the tone phonological rules of SY. Subsequent steps update the S-Tree by computing the numerical values of the prosody dimensions. To implement the intonation dimension, fuzzy control rules where developed based on data from native speakers of Yorùbá. The Classification And Regression Tree (CART) and the Fuzzy Decision Tree (FDT) techniques were tested in modelling the duration dimension. The FDT was selected based on its better performance. An important feature of our R-Tree framework is its flexibility in that it facilitates the independent implementation of the different dimensions of prosody, i.e. duration and intonation, using different techniques and their subsequent integration. Our approach provides us with a flexible and extendible model that can also be used to implement, study and explain the theory behind aspects of the phenomena observed in speech prosody

Prosody instruction for interpreter trainees: Does methodology make a difference? An experimental study

This study investigates the effect of explicit vs. implicit prosody teaching on the quality of consecutive interpretation by Farsi-English interpreter trainees. Three groups of student interpreters were formed. All were native speakers of Farsi who studied English translation and interpreting at the BA level at the University of Applied Sciences, Tehran, Iran. Participants were assigned to groups at random, but with equal division between genders (6 female and 6 male students in each group). No significant differences in English language skills (TOEFL scores) could be established between the groups. Participants took a pretest of consecutive interpreting before starting the program. The control group listened to authentic audio tracks and did exercises in consecutive interpreting. The fi rst experimental group received explicit instruction of English prosody and did exercises based on the theoretical explanation which was provided by their Iranian instructor. The second experimental group received implicit instruction of English prosody through the use of recasts. The total instruction time was the same for all the groups, i.e. 10 hours. Students then took a posttest in consecutive interpretation. The results showed that explicit teaching of prosody had a significantly positive effect on the overall quality of interpreting from Farsi into English compared with that of implicit prosody instruction. These results have pedagogical implications for curriculum designers, interpreter training programs, material producers and all who are involved in language study and pedagogy.Theoretical and Experimental Linguistic

Why Is Inflectional Morphology Difficult to Borrow?—Distributing and Lexicalizing Plural Allomorphy in Pennsylvania Dutch

: In this article we examine the allomorphic variation found in Pennsylvania Dutch plurality. In spite of over 250 years of variable contact with English, Pennsylvania Dutch plural allomorphy has remained largely distinct from English, except for a number of loan words and borrowings from English. Adopting a One Feature-One Head (OFOH) Architecture that interprets licit syntactic objects as spans, we argue that plurality is distributed across different √ root-types, resulting in stored lexical-trees (L-spans) in the bilingual mental lexicon. We expand the traditional feature inventory to be ‘mixed,’ consisting of both semantically-grounded features as well as ‘pure’ morphological features. A key claim of our analysis is that the s-exponent in Pennsylvania Dutch shares a syntactic representation for native and English-origin √ roots, although it is distinct from a ‘monolingual’ English representation. Finally, we highlight how our treatment of plurality in Pennsylvania Dutch, and allomorphic variation more generally, makes predictions about the nature of bilingual morphosyntactic representations

Why is inflectional morphology difficult to borrow?—Distributing and lexicalizing plural allomorphy in Pennsylvania Dutch

In this article we examine the allomorphic variation found in Pennsylvania Dutch plurality. In spite of over 250 years of variable contact with English, Pennsylvania Dutch plural allomorphy has remained largely distinct from English, except for a number of loan words and borrowings from English. Adopting a One Feature-One Head (OFOH) Architecture that interprets licit syntactic objects as spans, we argue that plurality is distributed across different root-types, resulting in stored lexical-trees (L-spans) in the bilingual mental lexicon. We expand the traditional feature inventory to be ‘mixed,’ consisting of both semantically-grounded features as well as ‘pure’ morphological features

A modular holistic approach to prosody modelling for Standard Yorùbá speech synthesis

This paper presents a novel prosody model in the context of computer text-to-speech synthesis applications for tone languages. We have demonstrated its applicability using the Standard Yorùbá (SY) language. Our approach is motivated by the theory that abstract and realised forms of various prosody dimensions should be modelled within a modular and unified framework [Coleman, J.S., 1994. Polysyllabic words in the YorkTalk synthesis system. In: Keating, P.A. (Ed.), Phonological Structure and Forms: Papers in Laboratory Phonology III, Cambridge University Press, Cambridge, pp. 293–324]. We have implemented this framework using the Relational Tree (R-Tree) technique. R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. The underlying assumption of this research is that it is possible to develop a practical prosody model by using appropriate computational tools and techniques which combine acoustic data with an encoding of the phonological and phonetic knowledge provided by experts. To implement the intonation dimension, fuzzy logic based rules were developed using speech data from native speakers of Yorùbá. The Fuzzy Decision Tree (FDT) and the Classification and Regression Tree (CART) techniques were tested in modelling the duration dimension. For practical reasons, we have selected the FDT for implementing the duration dimension of our prosody model. To establish the effectiveness of our prosody model, we have also developed a Stem-ML prosody model for SY. We have performed both quantitative and qualitative evaluations on our implemented prosody models. The results suggest that, although the R-Tree model does not predict the numerical speech prosody data as accurately as the Stem-ML model, it produces synthetic speech prosody with better intelligibility and naturalness. The R-Tree model is particularly suitable for speech prosody modelling for languages with limited language resources and expertise, e.g. African languages. Furthermore, the R-Tree model is easy to implement, interpret and analyse