1,311 research outputs found
A Duration-Dependent Regime Switching Model for an Open Emerging Economy
We employ duration-dependent Markov-switching vector auto-regression (DDMSVAR) methodology to construct an economic cycle model for an emerging economy. By modifying the software codes for DDMSVAR methodology written by Pelagatti (2003), we show how to estimate the economic cycles in an emerging economy where macroeconomic shocks are suddenly observed and their levels are deep. The monthly values of net international reserves, domestic debt, inflation and industrial production in the Turkish economy from January 1989 to July 2007 are used for constructing the empirical analysis. Empirical evidence shows that DDMSVAR model can be successfully used in an emerging economy to estimate the cycles using basic macroeconomic indicators.duration dependent regime switching model, economic cycles, Markov models, Turkish economy
Bayesian Models for Unit Discovery on a Very Low Resource Language
Developing speech technologies for low-resource languages has become a very
active research field over the last decade. Among others, Bayesian models have
shown some promising results on artificial examples but still lack of in situ
experiments. Our work applies state-of-the-art Bayesian models to unsupervised
Acoustic Unit Discovery (AUD) in a real low-resource language scenario. We also
show that Bayesian models can naturally integrate information from other
resourceful languages by means of informative prior leading to more consistent
discovered units. Finally, discovered acoustic units are used, either as the
1-best sequence or as a lattice, to perform word segmentation. Word
segmentation results show that this Bayesian approach clearly outperforms a
Segmental-DTW baseline on the same corpus.Comment: Accepted to ICASSP 201
The Impact of Code-Switching, Language Context, and Language Dominance on Suprasegmental Phonetics: Evidence for the Role of Predictability
Purpose: The present study investigates the suprasegmental reflexes of code-switching, considering both language context (i.e., language mode) and language dominance. Design: To this end, an experimental oral production paradigm was administered to 14 Spanish-English bilinguals, comparing code-switched to non-switched productions and varying both context (monolingual or bilingual) and response language (dominant or nondominant). Data and Analysis: Productions were analyzed for two suprasegmental features: pitch height and stressed vowel duration. Conclusions: Results indicate a significant effect of code-switching on suprasegmental production, with code-switched tokens produced with overall greater pitch movement and duration relative to non-switched tokens. These effects, however, were modulated by both language context and language dominance. Originality: Given the relation of prosody to cognitive factors, this novel approach to the suprasegmental features of code-switching, specifically considering language dominance and context, provides a unique opportunity to further the understanding of the underlying language switching process. Significance: These findings are addressed within a theoretical framework of predictability and hyper-articulation, and it is suggested that the suprasegmental realizations of code-switched tokens correspond to a degree of contextually driven predictability
Rhythm Metrics for 21 Languages
International audienc
Meta Learning Approach to Phone Duration Modeling
One of the essential prerequisites for achieving the naturalness of synthesized speech is the possibility of the automatic prediction of phone duration, due to the high importance of segmental duration in speech perception. In this paper we present a new phone duration prediction model for the Serbian language using meta learning approach. Based on the data obtained from the analysis of a large speech database, we used a feature set of 21 parameters describing phones and their contexts. These include attributes related to the segmental identity, manner of articulation (for consonants), attributes related to phonological context, such as segment types and voicing values of neighboring phones, presence or absence of lexical stress, morphological attributes, such as part-of-speech, and prosodic attributes, such as phonological word length, the position of the segment in the syllable, the position of the syllable in a word, the position of a word in a phrase, phrase break level, etc. Phone duration model obtained using meta learning algorithm outperformed the best individual model by approximately 2,0% and 1,7% in terms of the relative reduction of the root-mean-squared error and the mean absolute error, respectively
SMaTTS: standard malay text to speech system
This paper presents a rule-based text- to- speech
(TTS) Synthesis System for Standard Malay, namely SMaTTS. The
proposed system using sinusoidal method and some pre- recorded
wave files in generating speech for the system. The use of phone
database significantly decreases the amount of computer memory
space used, thus making the system very light and embeddable. The
overall system was comprised of two phases the Natural Language
Processing (NLP) that consisted of the high-level processing of text
analysis, phonetic analysis, text normalization and morphophonemic
module. The module was designed specially for SM to overcome
few problems in defining the rules for SM orthography system before
it can be passed to the DSP module. The second phase is the Digital
Signal Processing (DSP) which operated on the low-level process of
the speech waveform generation. A developed an intelligible and
adequately natural sounding formant-based speech synthesis system
with a light and user-friendly Graphical User Interface (GUI) is
introduced. A Standard Malay Language (SM) phoneme set and an
inclusive set of phone database have been constructed carefully for
this phone-based speech synthesizer. By applying the generative
phonology, a comprehensive letter-to-sound (LTS) rules and a
pronunciation lexicon have been invented for SMaTTS. As for the
evaluation tests, a set of Diagnostic Rhyme Test (DRT) word list was
compiled and several experiments have been performed to evaluate
the quality of the synthesized speech by analyzing the Mean Opinion
Score (MOS) obtained. The overall performance of the system as
well as the room for improvements was thoroughly discussed
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