Ioana Chitoran


Predicting non-native speech perception using the Perceptual Assimilation Model and state-of-the-art acoustic models
Juliette Millet | Ioana Chitoran | Ewan Dunbar
Proceedings of the 25th Conference on Computational Natural Language Learning

Our native language influences the way we perceive speech sounds, affecting our ability to discriminate non-native sounds. We compare two ideas about the influence of the native language on speech perception: the Perceptual Assimilation Model, which appeals to a mental classification of sounds into native phoneme categories, versus the idea that rich, fine-grained phonetic representations tuned to the statistics of the native language, are sufficient. We operationalise this idea using representations from two state-of-the-art speech models, a Dirichlet process Gaussian mixture model and the more recent wav2vec 2.0 model. We present a new, open dataset of French- and English-speaking participants’ speech perception behaviour for 61 vowel sounds from six languages. We show that phoneme assimilation is a better predictor than fine-grained phonetic modelling, both for the discrimination behaviour as a whole, and for predicting differences in discriminability associated with differences in native language background. We also show that wav2vec 2.0, while not good at capturing the effects of native language on speech perception, is complementary to information about native phoneme assimilation, and provides a good model of low-level phonetic representations, supporting the idea that both categorical and fine-grained perception are used during speech perception.


Allophonie et position dans la syllabe: Indices acoustiques pour les consonnes laterales (Acoustics of syllable position allophony: The case of lateral consonants)
Anisia Popescu | Ioana Chitoran
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP

L‟article traite de la manifestation acoustique des consonnes latérales en anglais américain et en roumain en fonction de la position syllabique et de la complexité phonotactique. Nous avons considéré quatre types de mesures: valeurs formantiques, équations locus, ratio d‟intensité et présence/absence de relâchements. Notre but est, d‟une part, de classifier les allophones des deux langues considérées et d‟autre part de déterminer les indices acoustiques des gestes articulatoires des consonnes latérales. Les résultats indiquent des différences importantes entre les deux langues. On montre que la distribution des allophones n‟est pas binaire, mais graduée et que le statut du geste dorsal peut être considéré comme un marqueur de « degré de clarté ». On montre aussi que l‟allophonie dépend de la position syllabique mais pas forcément de la complexité syllabique.


Using a machine learning model to assess the complexity of stress systems
Liviu Dinu | Alina Maria Ciobanu | Ioana Chitoran | Vlad Niculae
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We address the task of stress prediction as a sequence tagging problem. We present sequential models with averaged perceptron training for learning primary stress in Romanian words. We use character n-grams and syllable n-grams as features and we account for the consonant-vowel structure of the words. We show in this paper that Romanian stress is predictable, though not deterministic, by using data-driven machine learning techniques.


L’effet Labial-Coronal en italien (The Labial-Coronal effect in Italian) [in French]
Manon Carrissimo-Bertola | Nathalie Vallée | Ioana Chitoran
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP