PS: Introduction to Psycholinguistics

PS: Introduction to Psycholinguistics

Winter Term 2005/06

Instructor: Daniel Wiechmann

Office hours: Mon 2-3 pm

Email: [email protected]

Phone: 03641-944534

Web: www.daniel-wiechmann.net

Session 4:Understanding speech

Problems with recognition of speech Segmentation problem (how to seperate sounds

in speech) Possible remedies:

Possible-word constraint Metrical segmentation strategy Stress-based segmentation Syllable-based segmentation


Categorical perception Experiment Liberman et al. (1957)

Speech synthesizer creates continuum of artificial syllable that differ in the place of articulation of one phoneme

Subjects placed syllables into three categories (/b/, /d/, /g/)


Categorical perception voice onset time (VOT)

voiced and unvoiced consonants (e.g. /b/,/d/ vs /p/,/t/) differ with respect to VOT (difference ~ 60 ms)

Experimenters varied VOT on a scale (e.g. 30ms)Subjects make ‚either-or‘ distinctions


Categorical perception Selective adaptation

Repeated presentation of /ba/ makes people less sensitive to voicing feature (fatigue feature detector)

cut-off point for /b/-/p/ destinction shifts toward /p/-end of continuum


Prelexical (phonetic) vs postlexical (phonemic) code Prelexical code computed directly from

perceptual analysis (bottom-up) Postlexical coded is computed from higher-level

units such as words (top-down)Foss and Blank (1980) phoneme-monitoring taskBut cf. Foss and Gernsbacher (1983 and Marslen-

Wilson and Warren (1994)


In summary: There is a controversy about whether or not we

identify phonemes before we recognize higher level units (e.g. syllbles or words)


The role of context in identifying sounds: the phonemic restoration effect (cf. Warren and Warren 1970)


It was found that the *eel was on the orange It was found that the *eel was on the axle It was found that the *eel was on the shoe It was found that the *eel was on the table


It was found that the peel was on the orange It was found that the wheel was on the axle It was found that the heel was on the shoe It was found that the meal was on the table

Understanding speech

Phonemic restoration effect: 2 explanations 1. Context interacts directly with buttom-up

processes (sensitivity effect) 2. Context may simply provide additional source

of information (response bias effect)

Understanding speech:Samuel (1981, 1990)

Method: Subjects listen to sentences and meaningless noise was

presented during each sentence On some trials, noise was superimposed on one of the

phonemes of a word On other trials, phoneme was deleted Finally, sometimes phoneme was predicatble from context

Task decide whether or not crucial phoneme had been presented


Phonemic restoration effect: 2 explanationsHypotheses 1. If context improves sensitivity, then the ability to

dicriminate between phoneme plus noise and noise alone should be improved by predicatble context

2. If context affects response bias, then participants should simply be more likely to decide that the phoneme was presented when the word was presented in predictable context


Results: Context affected response bias but not

sensitivity Contextual information does not have a direct

effect on bottom-up processing

Understanding speech:Models of speech recognition

Most influential models Motor theory (Libermann et al 1967)

Listeners mimic the articulatory movements of the speaker

Cohort theory (Marslen-Wilsen and Tyler 1980) TRACE model (McClelland and Elman 1986)

Understanding speech:Models of speech recognition: neurons

Understanding speech:Models of speech recognition: neuron (schematic)

Synapse:The junction across which a nerve impulse passes from an axon terminal to a neuron

Understanding speech:Models of speech recognition: neuronal networks

The brain is composed of over 10-100 billion nerve cells, or neurons, that communicate with one another through specialized contacts called synapses.

Typically, a single neuron receives 2000-5000 synapses from other neurons; these synapses are located almost exclusively on the neuron's dendrites, long projections that radiate out from the neuron's cell body.

In turn, the neuron's axon, a long thin process that grows out from the cell body of a neuron, makes synaptic connections with 1000 other neurons. In this way, neuronal signals pass from neuron to neuron to form extensive and elaborate neural circuits.

Understanding speech:Models of speech recognition: number of neurons

human brain

Understanding speech:Models of speech recognition: introducing

connectionist models



Two central assumptions artificial neural nets (ANN):1) processing occurs through the action of many

simple, interconnected processing units (neurons)

2) activation spreads around the network in a way determined by the strength of the links, i.e. the connections between units



Some models learn back-propagation

Some don‘t Interactive activation model

(IAC) McClelland and Rumelhart (1981) does not learn

TRACE model (McClelland and Elman 1986) is an IAC model

Understanding speech:Models of speech recognition: from neural

networks to connectionist models

Threshold: the total amount of activation needed to make the node fire

Connections (or links) have different weights

Connections can be inhibitory or excitatory(facilitatory)



Threshold: 1.0 -1 to +1

+ 0.6 (excitatory)

- 0.5 (inhibitory)

+ 0.7 (excitatory)

Ergo: no firing



Threshold: 1.0 -1 to +1

- 0.5+ 0.9 (excitatory)

- 0.2 (inhibitory)

+ 0.4 (excitatory)

Ergo: firing

- 0.9

+ 0.5

-1 to +1





Interactive activation network (McClelland and Rumelhart 1981)

Understanding speech:Models of speech recognition: TRACE

TRACE model (McClelland and Elman 1986) There are individual processing units, or nodes,

at three different levels:FEATURES (place & manner of production, voicing)PHONEMESWORDS


TRACE model (McClelland and Elman 1986) Feature nodes are connected to phoneme nodes Phoneme nodes are connected to word nodes

Connections between levels operate in both directions, and are only facilitatory (i.e. no inhibition)


TRACE model (McClelland and Elman 1986) There are connections among units or nodes at

the same level These connections are inhibitory


TRACE model (McClelland and Elman 1986) Nodes influence each other in proportion to their

activation levels and the strength of their interconnections

As excitation and inhibition spread among nodes, a pattern of activation, or TRACE, develops


TRACE model (McClelland and Elman 1986) The word that is recognized is determined by the

activation level of the possible candidate words.

Documents

PS: Introduction to Psycholinguistics