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Deep Learning > Word Recognition > Flashcards

Word Recognition Flashcards

1
Q

COHORT’s assumptions (per TRACE)

A
  • Uses first sound or first CV to determine words in initial candidate set (“cohort”).
  • Eliminates words from cohort as successive phonemes arrive.
    • Phoneme-to-word inhibition
    • Words can also be eliminated based on semantic content, but initial cohort is determined by acoustics.
  • Word recognition occurs when there is a single item ileft n candidate set.
  • Word recognition can influence phoneme identification after the word has been recognized.
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2
Q

COHORT person

A

Marslen-Wilson, circa 1980

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3
Q

Good evidence for COHORT

A

In gating, the uniqueness point usually matches the participant’s acceptance/certainty point.

Confirms COHORT’s prediction that words are recognized when just one item remains in cohort.

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4
Q

COHORT’s problems (per TRACE)

A
  • Cannot cope with distortion or underspecified onsets
  • No way to recover items that were removed from cohort
    • Want to reject “present” for pleasant but accept “blacelet” for bracelet.
  • Assumes listeners always knows where a word’s onset is.
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5
Q

COHORT’s three stages of word recognition

A
  1. Access (when the bottom-up perceptual input first activates lexical representations)
  2. Selection (narrowing down the activation candidate set)
  3. Integration (retrieve and integrate semantic/syntactic details)

COHORT

  1. accesses all items consistent with input and
  2. evaluates the multiple words in parallel,
  3. using information as it becomes available.
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6
Q

COHORT’s segmentation strategy

A

Segmentation is implicit.

Utterance onset marks onset of first word.

Offset of each word marks onset of next word.

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7
Q

Controversial features of TRACE

A

Spatializing time, so lots of units are duplicated.

Assuming interactive effects between layers.

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8
Q

Activation in TRACE

A

It’s continuous, based on how acoustic/phonetic features map onto lexical representations.

Supports partial activation rhymes because they are bottom-up matches.

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9
Q

Interactivity in TRACE

A
  • Top-down connections from lexical items to phonemes.
  • The top-down connections from phonemes to feature detectors are usually disabled.
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10
Q

Competition in TRACE

A

Temporally overlapping units in phonemic and lexical layers inhibit one one another.

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11
Q

How TRACE models time

A

Units in phoneme and lexical layers are repeated every few time slices.

It spatializes time.

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12
Q

Coarticulation in TRACE

A

Input phoneme’s features are spread over 11 steps, but the centers of adjacent input phonemes are 6 steps apart.

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13
Q

TRACE’S acoustic features

A
  • Acute
  • Burst
  • Consonantal
  • Diffuse
  • Power
  • Vocalic
  • Voiced

Each with nine levels of activation, each with a feature detector at every timestep.

So there would be Voice0, Voice1, …, Voice8 feature detector units at each step.

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14
Q

Shortlist’s main idea

A
  • At each phoneme time-step, a shortlist of matching words is generated
  • The words in the shortlists that overlap each other compete with each other via lateral inhibition
  • Separates lexical access (shortlist formation based on match scores) from competition (overlapping words across the lists have to compete with each other)
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15
Q

Shortlist’s scoring system

A
  • 1 point for each matching phoneme
  • -3 points for each mismatching phoneme
  • Strong mismatch penalty will keep mostly onset-matching items in the shortlist.
  • Rhymes will only appear in the list
    • when shortlist is sparse and
    • when there have been multiple matching phonemes to overcome initial mismatch.
  • There is a shortlist at each phoneme timestep, consisting of words with top match scores.
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16
Q

Simple recurrent networks

A
  • Learning network
  • Layers for input units, hidden units, context units, output units
  • Context units are exact copy of last time steps hidden units
  • Hidden units combine information from input and previous state.
  • Interactive in the sense that the context interacts with the input units.
    • Recurrence is self feedback
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17
Q

Overtraining in SRNs

A
  • If you train an SRN until error asymptotes, it will not show rhyme effects
  • If you train until each target reaches from recognition threshold, rhyme effects will remain intact.
  • Adults learning novel neighborhoods look like these SRNs (Magnuson et al. 2003)
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18
Q

Localist representations

A

One unit for each word.

Competing units compete as their activation changes over time.

19
Q

Distributed representations

A
  • All items are represented by a shared set of units.
  • Competition shows up in the blend of hidden representations.
  • Makes predictions priming effects of ambiguous inputs activates a blend of competitors.
20
Q

Distributed Cohort Model

A

SRN but two output layers: phonological and lexical semantics

Distributed representation of phonological and semantic features in the hidden units

21
Q

Cross-Modal Semantic Priming

A
  • Castle-candy-sweet priming
  • Castle activates its cohort (including candy) which in turn activate semantic cohorts (including sweet).
  • Works for cohorts but not for rhymes
    • Cohorts don’t get much inhibition during start of word
    • When rhymes finally have perceptual support, the onset-cohort are strongly inhibiting them
22
Q

Naming / Repetition / Shadowing

A
  • Play a wordlike stimulus. Listener repeats word.
  • Measure accuracy and response latency.
  • Word frequency and neighborhood density influence outcome measures.
  • Unlike real recognition
    • bc it’s post-perceptual
    • bc listener may pay close attention to the speech sounds (without semantic processing).
23
Q

Lexical Decision

A
  • Play a wordlike stimulus. Listener decides whether stimulus is a word or not.
  • Measure accuracy and response latency.
  • Sensitive to
    • word frequency (more false rejects of less common words)
    • neighborhood density (probably, slower to make decision when stimulus overlaps with a lot of words).
  • Unlike real recognition bc
    • it’s a post-perceptual judgment about stimulus.
    • Also a listener can make decision w/o knowing meaning of word (I can recognize tamp but I don’t know what it means).
24
Q

Gating

A
  • Early gates have a wide variety of guess (many words activated)
  • Uniquess point: When there is only one completion of a word
  • Recognition point: When correct guessing occurs
  • Recognition can precede uniqueness, especially for higher frequeny words
25
Q

DAS similarity

A
  • Neighborhood consists of all words that differ by Deletion, Addition, or Substitution of just one phoneme
  • Treat rhymes as just as neighborly as cohorts.
26
Q

Cohort similarity

A

Neighborhood consists of all words that share first 1-2 phonemes

27
Q

Graded similarity metric

A
  • Neighborhood based on phonetic features
  • cat/pat (1 feature diff) more similar than cab/bat (2 feature diff)
  • bull/veer are neighbors (3 feature diff)
28
Q

Frequency-Weighted Neighborhood Probability

A
  • Frequency weighted similarity score: Similarity of candidate to input times candidate’s log frequency: FWSS(t,x) = f(t) * S(t,x)
  • Neighborhood probability is a candidate’s FWSS divided by sum of FWSS of all other words in lexicon
  • For DAS similarity, S(t,x) is 1 or 0, so it reduces to FWNP(t) = f(t) / ∑ f(w)
    • Proportion of neighborhood frequency contributed by the word t
  • Frequency-weighting applies a prior probability
  • If 2 words matched on neighborhood size, more frequent one is easier to recognize
  • If 2 words matched on frequency, one in less dense neighborhood is easier to recognize
29
Q

Principles of spoken word recognition

A
  • Multiple activation: Multiple candidate words are activated
  • Activation of candidate word is based on
    • Similarity: degree of fit between speech and candidate
    • Priors: prior probability of word
  • Compeition: Competition among candidates leads to recognition
30
Q

How to test model against data

A
  • Establish linking hypothesis between model and data
  • Figure out if success/failure is due to theory, implemenation (input representation, number of units, etc), model parameters, or linking hypothesis
31
Q

How to model frequency in TRACE

A
  1. Post-lexical (adjust choice rule that transforms activations)
  2. Resting activations
  3. Manipulate phoneme-to-word weights (effect of frequency is proportional to amount of word heard)

All very similar results (Dahan et al., 2001) but bottom-up weight scheme had best fit to data.

32
Q

Arguments for feedback (as in TRACE)

A
  • We see lexical effects on sublexical tasks
  • Makes model robust to noise
  • Implicitly encodes sublexical probabilities (diphone etc)
  • TRACE more accurate and faster if feedback on
  • Top-down knowledge can guide perceptual recalibration
  • Interactive activation systems can perform optimal Bayesian inference
33
Q

Arguments against feedback (in TRACE)

A
  • Lexical effects can be captured if perceptual and lexical info integrated postlexically
  • No way to increase information in raw signal, so best bottom-up signal is best guess
  • Predicts perceptual hallucinations
    • But listeners to show lexically influence perceptions, like phoneme restoration or failing to recognize some mispronunciations
34
Q

Magnuson, Dixon, Tanenhaus, and Aslin, 2007

A
  • Eyetracking
  • Demonstrated competition effects even though competitors were not displayed
  • Manipulated frequency, neighborhood density (one phoneme DAS), and cohort density
    • High frequency: Early, continuous facilitating effect
    • High cohort density: Early, continuous inhibitory effect
    • High neighbor density: Early facilitating, late inhibitory effect (rhymes kick in)
  • Competitor set dynamically changes as word unfolds
35
Q

Dahan, Magnuson, Tanenhaus, and Hogan, 2001

A
  • Subcategorical phonetic mismatches
  • Ne(ck)t slower than ne(p)t in eyetracking
  • Marslen-Wilson and Warren, 1994, found no RT difference for these kind of data.
    • Concluded part of “neck” could not be inhibiting “net”
  • Here, eyetracking data shows the predicted difference.
  • Compatible with lateral inhibition
36
Q

Phonotactics in TRACE

A

Implicit

More frequently a phoneme or n-phone appears in TRACE lexicon, the more top-down feedback it gets

37
Q

Hannagan, et al., 2013

A
  • Implemented an activation model inspired by TRACE but without the spatial duplication of time
    • Used string kernels
    • Achieves spatial invariance for visual word recognitions, so generalize to time.
  • Layers
    • Input phonemes over time
    • 1-Phone and 2-Phone levels
    • Word level (lateral inhibition here)
    • /d/o/g/ activates /do/, /dg/, /og/ but not /gd/
  • Proof of concept
38
Q

Temporal problems for spoken word recognition models

A
  1. Temporal order over phonemes: dog vs. god
  2. Multi-token independence problem: do vs. dude
  3. Temporal order over words: man bites dog vs. dog bites man
  4. Segmentation problem
39
Q

Phenomena simulated by TRACE

A
  • Lexical effects
    • Main Ganong effect: Xlug as /p/, Xlood as /b/
    • Time pressure eliminates effect
    • Late lexical effects (targeX as /t/). Context has stronger effect for ambiguous phonemes late in word.
    • No lexical effects for unambiguous phonemes
  • Phoneme monitoring data
    • No lexical effect on RT for word-initial targets (respond when you hear word starting with /g/)
    • Lexical effect on RT for later targets (faster on secreT vs. gulduT)
  • Lexical conspiracy: Phonotactic effects from lexical statistics
    • Xluly (X in p-t continuum). Interprets as “Xl” early on then shifts to “tl” by end, a phonotactically illegal sequence.
  • Simulating cohort and rhyme competition in eyetracking
  • Sensitivity to subcategorical mismatches
  • Lexical basis for segmentation
40
Q

The Shortlist Models

A
  • Shortlist A, Merge, Shortlist B
  • Differentiates time-specific tokens and time-invariant words
  • Generates a shortlist of tokens and lexical lattice on the fly
    • How could lattice be wired on the fly?
  • Core theoretical Assumptions
    • Prelexical to Lexical paths are feedforward only
    • Candidate selection is based on matching and mismatching information
    • Implicit segmentation via competition among candidates
41
Q

similarity in neighborhood activation model

A
  • Global similarity
  • Static set of competitors
  • Recognition RT related frequency-weighted neighborhood probability
    • More neighbors, harder.
    • More frequent neighbors, harder.
  • Naming, decision, recognition in noise allow global similarity to matter
42
Q

why input to spoken word models should not be phoneme units

A

They overlap too much.

Phoneme certainty is never 100% at any one point in time.

43
Q

some ways to make non-phoneme input to word recognition models

A
  1. Fake acoustics
  2. Real acoustics
  3. Simulate output of prelexical processing
44
Q

Shortlist B

A
  • Norris and McQueen 2007
  • Most radical move: Abandons activation entirely
    • What is an activation, anyway?
  • No inhibition among competing hypotheses
  • Competition reflected in differing path probabilities
  • Assumes word recognition is optimal, in a Bayesian sense
  • As ambiguity increases, prior beliefs matter more.
  • Priors require us to encode frequency.
  • Likelihood functions are more important than similarity metrics

Deep Learning (4 decks)

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