AIMC Topic: Phonetics

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Evaluating the consistency of lenition measures: Neural networks' posterior probability, intensity velocity, and duration.

The Journal of the Acoustical Society of America
Predictions of gradient degree of lenition of voiceless and voiced stops in a corpus of Argentine Spanish are evaluated using three acoustic measures (minimum and maximum intensity velocity and duration) and two recurrent neural network (Phonet) meas...

Decision Tree Versus Linear Support Vector Machine Classifier in the Screening of Medial Speech Sounds: A Quest for a Sound Rationale.

Studies in health technology and informatics
This paper describes the latest development in the classification stage of our Speech Sound Disorder (SSD) Screening algorithm and presents the results achieved by using two classifier models: the Classification and Regression Tree (CART)-based model...

Japanese Braille Translation Using Deep Learning - Conversion from Phonetic Characters (Kana) to Homonymic Characters (Kanji).

Studies in health technology and informatics
A blind student writes and submits reports in Braille word processor, which is difficult for teachers to read. This study's purpose is to make a translator from Braille into mixed Kana-Kanji sentences for such teachers. Because Kanji has homonyms, it...

A Cross-Modal and Cross-lingual Study of Iconicity in Language: Insights From Deep Learning.

Cognitive science
The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of ty...

Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input.

Proceedings of the National Academy of Sciences of the United States of America
Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get bet...

Highlighting interlanguage phoneme differences based on similarity matrices and convolutional neural network.

The Journal of the Acoustical Society of America
The goal of this research is to find a way of highlighting the acoustic differences between consonant phonemes of the Polish and Lithuanian languages. For this purpose, similarity matrices are employed based on speech acoustic parameters combined wit...

Phonetic variability constrained bottleneck features for joint speaker recognition and physical task stress detection.

The Journal of the Acoustical Society of America
Normalizing intrinsic variabilities (e.g., variability in speech production brought on by aging, physical or cognitive task stress, Lombard effect, etc.) in speech and speaker recognition models is essential for system robustness. This study focuses ...

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition.

Cognitive science
Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-ste...

Density and Distinctiveness in Early Word Learning: Evidence From Neural Network Simulations.

Cognitive science
High phonological neighborhood density has been associated with both advantages and disadvantages in early word learning. High density may support the formation and fine-tuning of new word sound memories-a process termed lexical configuration (e.g., ...

Semi-supervised learning of a nonnative phonetic contrast: How much feedback is enough?

Attention, perception & psychophysics
Semi-supervised learning refers to learning that occurs when feedback about performance is provided on only a subset of training trials. Algorithms for semi-supervised learning are popular in machine learning because of their minimal reliance on labe...