Ini Apel Ni Nya ‘This Here Apple Now' Deictics in the Malay Speech of Southwest Malukan Migrants in the Netherlands1

Dialek Melayu yang dipakai para pendatang asal Maluku Selatan di Belanda inimemperlihatkan rangkaian demonstrativa dan endofora yang tidak ditemukandalam bahasa Indonesia baku. Makalah ini mengkaji semantik dari rangkaianunsur deiktik tersebut dalam kerangka linguistik kognitif dan menjelaskannyasebagai sesuatu yang muncul dari bahasa ibu penutur, dengan mencontohkanbahasa Meher dan Leti. Makalah ini ditutup dengan mengaitkan penemuannyadengan bahasa Melayu Tangsi yang diduga adalah nenek moyang daridialek Melayu yang digunakan pendatang Maluku di Belanda. Dinyatakanbahwa pencarian asal-usul dialek turunan Melayu Pijin sejenis ini hanya bisadilakukan dengan berfokus pada makna yang disampaikan lewat konstruksikonstruksinya

Ini Apel Ni Nya ‘This Here Apple Now' Deictics in the Malay Speech of Southwest Malukan Migrants in the Netherlands1

Dialek Melayu yang dipakai para pendatang asal Maluku Selatan di Belanda inimemperlihatkan rangkaian demonstrativa dan endofora yang tidak ditemukandalam bahasa Indonesia baku. Makalah ini mengkaji semantik dari rangkaianunsur deiktik tersebut dalam kerangka linguistik kognitif dan menjelaskannyasebagai sesuatu yang muncul dari bahasa ibu penutur, dengan mencontohkanbahasa Meher dan Leti. Makalah ini ditutup dengan mengaitkan penemuannyadengan bahasa Melayu Tangsi yang diduga adalah nenek moyang daridialek Melayu yang digunakan pendatang Maluku di Belanda. Dinyatakanbahwa pencarian asal-usul dialek turunan Melayu Pijin sejenis ini hanya bisadilakukan dengan berfokus pada makna yang disampaikan lewat konstruksikonstruksinya

Using Resources from a Closely-related Language to Develop ASR for a Very Under-resourced Language: A Case Study for Iban

International audienceThis paper presents our strategies for developing an automatic speech recognition system for Iban, an under-resourced language. We faced several challenges such as no pronunciation dictionary and lack of training material for building acoustic models. To overcome these problems, we proposed approaches which exploit resources from a closely-related language (Malay). We developed a semi-supervised method for building the pronunciation dictionary and applied cross-lingual strategies for improving acoustic models trained with very limited training data. Both approaches displayed very encouraging results, which show that data from a closely-related language, if available, can be exploited to build ASR for a new language. In the final part of the paper, we present a zero-shot ASR using Malay resources that can be used as an alternative method for transcribing Iban speech

Investigating spoken emotion : the interplay of language and facial expression

This thesis aims to investigate how spoken expressions of emotions are influenced by the characteristics of spoken language and the facial emotion expression. The first three chapters examined how production and perception of emotions differed between Cantonese (tone language) and English (non-tone language). The rationale for this contrast was that the acoustic property of Fundamental Frequency (F0) may be used differently in the production and perception of spoken expressions in tone languages as F0 may be preserved as a linguistic resource for the production of lexical tones. To test this idea, I first developed the Cantonese Audio-visual Emotional Speech (CAVES) database, which was then used as stimuli in all the studies presented in this thesis (Chapter 1). An emotion perception study was then conducted to examine how three groups of participants (Australian English, Malaysian Malay and Hong Kong Cantonese speakers) identified spoken expression of emotions that were produced in either English or Cantonese (Chapter 2). As one of the aims of this study was to disambiguate the effects of language from culture, these participants were selected on the basis that they either shared similarities in language type (non-tone language, Malay and English) or culture (collectivist culture, Cantonese and Malay). The results showed that a greater similarity in emotion perception was observed between those who spoke a similar type of language, as opposed to those who shared a similar culture. This suggests some intergroup differences in emotion perception may be attributable to cross-language differences. Following up on these findings, an acoustic analysis study (Chapter 3) showed that compared to English spoken expression of emotions, Cantonese expressions had less F0 related cues (median and flatter F0 contour) and also the use of F0 cues was different. Taken together, these results show that language characteristics (n F0 usage) interact with the production and perception of spoken expression of emotions. The expression of disgust was used to investigate how facial expressions of emotions affect speech articulation. The rationale for selecting disgust was that the facial expression of disgust involves changes to the mouth region such as closure and retraction of the lips, and these changes are likely to have an impact on speech articulation. To test this idea, an automatic lip segmentation and measurement algorithm was developed to quantify the configuration of the lips from images (Chapter 5). By comparing neutral to disgust expressive speech, the results showed that disgust expressive speech is produced with significantly smaller vertical mouth opening, greater horizontal mouth opening and lower first and second formant frequencies (F1 and F2). Overall, this thesis provides an insight into how aspects of expressive speech may be shaped by specific (language type) and universal (face emotion expression) factors

Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages

This paper provides an overall introduction of our Automatic Speech Recognition (ASR) systems for Southeast Asian languages. As not much existing work has been carried out on such regional languages, a few difficulties should be addressed before building the systems: limitation on speech and text resources, lack of linguistic knowledge, etc. This work takes Bahasa Indonesia and Thai as examples to illustrate the strategies of collecting various resources required for building ASR systems.Comment: Published by the 2017 IEEE International Conference on Orange Technologies (ICOT 2017

Current Developments in Comparative Austronesian Studies

For speakers of Austronesian languages, there has been, for millennia, an intuitive recognition of the connections among related languages. These intuitions are a key part of the capacities that have allowed speakers of different Austronesian languages to communicate with one another, that have facilitated the migration of individuals and groups among different speech communities and that have fostered mutual interrelations among speech communities. All these factors now contribute to making the study of Austronesian languages a challenging comparative field of study. Comparative Austronesian studies are comprised of a variety of disciplines: linguistics, archaeology, anthropology, history and, in recent years, biological and genetic research. Each of these disciplines contributes new perspectives to an understanding of the Austronesian-speaking world. The emergence of such comparative Austronesian research is a recent coalescence of a long development that began with an initial and partial recognition of relations among Austronesian languages. While native speakers often intuitively recognize relations among the languages they use or encounter in their daily lives, no speaker of these languages can grasp the diversity of these thousands of languages nor trace the historical underpinnings of the great variety of speech communities that make up the Austronesian-speaking world. It is precisely because the Austronesian languages had spread so widely from Taiwan to Timor and from Madagascar to Easter Island that comprehension of the relations among these languages came about in stages beginning with voyages in the 16th century

Zero resource speech synthesis using transcripts derived from perceptual acoustic units

Zerospeech synthesis is the task of building vocabulary independent speech synthesis systems, where transcriptions are not available for training data. It is, therefore, necessary to convert training data into a sequence of fundamental acoustic units that can be used for synthesis during the test. This paper attempts to discover, and model perceptual acoustic units consisting of steady-state, and transient regions in speech. The transients roughly correspond to CV, VC units, while the steady-state corresponds to sonorants and fricatives. The speech signal is first preprocessed by segmenting the same into CVC-like units using a short-term energy-like contour. These CVC segments are clustered using a connected components-based graph clustering technique. The clustered CVC segments are initialized such that the onset (CV) and decays (VC) correspond to transients, and the rhyme corresponds to steady-states. Following this initialization, the units are allowed to re-organise on the continuous speech into a final set of AUs in an HMM-GMM framework. AU sequences thus obtained are used to train synthesis models. The performance of the proposed approach is evaluated on the Zerospeech 2019 challenge database. Subjective and objective scores show that reasonably good quality synthesis with low bit rate encoding can be achieved using the proposed AUs