Learning phonetic categories by tracking movements

Gauthier, Bruno; Shi, Rushen; Xu, Yi

doi:10.1016/j.cognition.2006.03.002

Cited by 51 publications

(70 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar results were obtained when training four-unit maps with F0 adult-directed input containing less contextual variability 29 than the materials used in the present study. Even a 900-unit map (30 Â 30) trained with similar data (the ADS input) as in the present study could not group the F0 input into distinct tonal clusters.…”

Section: Simulation 1-f0supporting

confidence: 77%

“…The neighborhood function included all units at training onset, decreased exponentially, and only included the best matching unit (BMU) at the end of training, i.e., the closest unit to the input in terms of Euclidean distance (see details in Refs. 29,30).…”

Section: Training and Testingmentioning

confidence: 99%

“…D1 normalizes between-speaker F0 differences and contextual pitch variability by focusing on the dynamic movement of F0 towards each tonal target. 29 A second simulation assessed whether or not IDS velocity profiles help tonal categorization better than ADS velocity profiles.…”

Section: Simulation 1-f0mentioning

confidence: 99%

See 2 more Smart Citations

A connectionist study on the role of pitch in infant-directed speech

Gauthier

Shi

2011

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

Infant-directed speech (IDS) is believed to facilitate language learning. However, the benefit may be either due to clearer acoustic correlates to linguistic structures, or simply increased attention from infants induced by IDS exaggerated prosody. This study investigated the pure effect of IDS pitch on lexical tone learning, with attentional/ affective factors removed by using artificial neural networks. Following training with the pitch of Mandarin tones in IDS versus adult-directed speech, the networks yielded equal tonal categorization for both registers. IDS pitch produced no additional linguistic support. IDS pitch appears to strictly play the non-linguistic role of attention/affect, which may indirectly benefit learning.

show abstract

Section: Simulation 1-f0supporting

confidence: 77%

Section: Training and Testingmentioning

confidence: 99%

See 1 more Smart Citation

A connectionist study on the role of pitch in infant-directed speech

Gauthier

Shi

2011

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

show abstract

“…They then showed that the distribution of learned receptive fields exhibited the perceptual magnet effect humans exhibit in the perception of the vowels of their native language. Another example is a study by Gauthier et al ͑2007͒ that used a SOM to successfully classify Mandarin tones based on the first derivative of f 0 contours. This classification was robust in the face of the surface variability present in the multiple speakers' connected speech from which the inputs were taken.…”

Section: B Previous Applications Of Neural Network To Related Problemsmentioning

confidence: 99%

Data-driven automated acoustic analysis of human infant vocalizations using neural network tools

Warlaumont

Oller

Buder

et al. 2010

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

Acoustic analysis of infant vocalizations has typically employed traditional acoustic measures drawn from adult speech acoustics, such as f 0 , duration, formant frequencies, amplitude, and pitch perturbation. Here an alternative and complementary method is proposed in which data-derived spectrographic features are central. 1-s-long spectrograms of vocalizations produced by six infants recorded longitudinally between ages 3 and 11 months are analyzed using a neural network consisting of a self-organizing map and a single-layer perceptron. The self-organizing map acquires a set of holistic, data-derived spectrographic receptive fields. The single-layer perceptron receives self-organizing map activations as input and is trained to classify utterances into prelinguistic phonatory categories ͑squeal, vocant, or growl͒, identify the ages at which they were produced, and identify the individuals who produced them. Classification performance was significantly better than chance for all three classification tasks. Performance is compared to another popular architecture, the fully supervised multilayer perceptron. In addition, the network's weights and patterns of activation are explored from several angles, for example, through traditional acoustic measurements of the network's receptive fields. Results support the use of this and related tools for deriving holistic acoustic features directly from infant vocalization data and for the automatic classification of infant vocalizations.

show abstract

“…Gauthier et al, 2007 for Mandarin) and are not motivated by phonological theory. Such models contain fine grained representation of tone; i.e.…”

Section: Fig1: Examples Of F0 Pitch Tracks On 5 Thai Words With Idenmentioning

confidence: 99%

Probabilistic Modeling of Tone Perception: Autosegmental ‘targets’ are insufficient

Ramadoss

2011

ExAbs

View full text Add to dashboard Cite

show abstract

Learning phonetic categories by tracking movements

Cited by 51 publications

References 58 publications

A connectionist study on the role of pitch in infant-directed speech

A connectionist study on the role of pitch in infant-directed speech

Data-driven automated acoustic analysis of human infant vocalizations using neural network tools

Probabilistic Modeling of Tone Perception: Autosegmental ‘targets’ are insufficient

Contact Info

Product

Resources

About