Frenchay Dysarthria Assessment: What

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Frenchay Dysarthria Assessment Whats new?

• Rebecca Palmer, Pam Enderby, James Carmichael

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Topics

• Original FDA overview
• Advantages and disadvantages of this assessment
• FDA 2 new aspects
• Computerised FDA
• Demonstration
• Current work on automated intelligibility testing

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Original FDA

• Author Pam Enderby
• First published in 1983
• Result of research identifying nature and
  patterns of oromotor movements associated with
  different neurological diseases (Enderby 1983)
• Translated into French, German, Dutch, Norwegian,
  Swedish, Finnish, Catalan and Castilian

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Aim of FDA

• To analyse several important parameters of the
  motor speech system
• To guide treatment
• To assist with neurological diagnosis
• To have good reliability and validity between and
  within clinicians without extensive training

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Structure of FDA

• Reflexes
• Cough, swallow, dribble/drool
• Respiration
• At rest, in speech
• Lips
• At rest, spread, seal, alternate, in speech
• Palate
• Fluids, maintenance, in speech
• Laryngeal
• Time, pitch, volume, in speech
• Tongue
• At rest, protrusion, elevation, lateral,
  alternate, in speech
• Intelligibility
• Words, sentences, conversation

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Procedure

• Ask patient to carry out a task
• Rate ability of each parameter using a 9 point
  scale 5 descriptors ½ marks

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Advantages of FDA

• Intelligibility commonly used to assess severity
  of dysarthria and to monitor progress BUT
  Intelligibility measures alone do not diagnose
  type of dysarthria or guide treatment
• FDA breaks speech up into its component parts so
  the clinician can analyse what contributes to the
  reduced intelligibility thus guiding treatment
• FDA provides a profile that contributes to the
  neurological diagnosis

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Disadvantages of FDA

• Some measures can be subjective
• Some descriptors are interpreted differently by
  different clinicians reducing reliability
• Intelligibility section
• Too few words/sentences ?regular users can learn
  them
• Sentence structure the man is therefore only
  listening for the last word
• Scoring system based on number listener
  understood out of 10 (crude)

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FDA 2

• Authors Pam Enderby Rebecca Palmer
• 2008
• Aim To address theoretical and practical issues
  identified in reviews of the first edition

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Improvements 1

• Omitted items that have been found to be
  unreliable or redundant to the purposes of
  diagnosis and treatment
• e.g. Jaw tests patients rarely have abnormality
  in the jaw therefore the information didnt
  assist diagnosis

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Improvements 2

• Improved reliability of descriptors
• Inter-rater reliability testing between
  experienced users of the FDA showed that some
  descriptors were interpreted differently.
• E.g. voice time
• Patient can say ah for 15 seconds
• Patient unable to sustain clear voice for 3
  seconds
• Constant hoarse voice RP a), PE e)

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Improvements 2

• Inter rater and test retest reliability
• Audio recordings of 9 people with a range of
  types and severities of dysarthria performing the
  audible FDA 2 tests
• 6 speech therapists working with a mixed adult
  caseload judged 42 examples of FDA 2 tests.
• Scored on a 9 point scale
• Same 42 tests presented again to the listeners
  after 6 week interval
• Inter and intra rater reliability were calculated
  using intra class correlation coefficients

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Inter and intra judge reliability
Judge 1 2 3 4 5 6
1 0.76
2 0.77 0.92
3 0.56 0.65 0.72
4 0.67 0.60 0.51 -
5 0.38 0.52 0.49 0.79 0.73
6 0.66 0.72 0.70 0.49 0.56 0.76
Criteria for interpretation of reliability
coefficients for ordinal measures (Landis Koch,
1977) lt0 poor, 0.01-0.20 slight,
0.21-0.40 fair, 0.41-0.60 moderate (mod),
0.61-0.80 substantial (sub) 0.81 1 almost
perfect (per)
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Improvements 3

• In speech tests
• Sound saturated sentences provided for patient to
  say so that clinician can listen to the accuracy
  of sound placement in speech
• Lips in speech
• Mary brought me a piece of maple syrup pie
• Tongue in speech
• Kenneths dog took ten tiny ducks today

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Improvements 4

• Intelligibility testing
• New set of words
• Corpus of 116 words to reduce probability of
  listeners learning the words with increased
  exposure
• Phonetically balanced list for types of sounds,
  position of sounds in words, word length
• Word frequency gt10 per million to control for any
  effects of word frequency on intelligibility

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Improvements 4

• Sentence intelligibility
• Key words phonetically balanced to account for
  place, manner, position and word length
• Carrier phrases/sentences are all different so
  the listener has to listen to a sentence, not
  just interpret the key word in a standard carrier
  phrase
• Can you go the shop?
• My daughter is a nurse
• Lets go to the theatre

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Availability

• FDA 2 available now from Pro-ed
• Only in English!

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Computerised FDA

• James Carmichael produced computer version
• Demonstration

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Planned additions to CFDAAutomation of
intelligibility testing modelling the naiive
listener

• If the learning effect alters a listeners
  perception of a particular individuals speaking
  style, is that listeners judgement still
  representative of the naïve listener?
• Can a computer model be built which behaves like
  an eternal naïve listener (i.e. never adapting
  to an unfamiliar speaking style and therefore
  always consistent in assessment)?

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Using HMM Models to Emulate the Naïve listener

• A hidden Markov Model (HMM)
• a statistical representation of a speech unit at
  the phone/word/utterance level.
• HMM models are trained by analysing the
  acoustic features of multiple utterances
  representing the specified speech unit.

Multiple Speech Samples from multiple speakers
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Goodness of fit

• Once trained, an HMM word model can be used to
  estimate the likelihood that a given speech sound
  could have actually been produced by that word
  model.
• This likelihood is called a goodness of fit (GOF)
  
• expressed as a log likelihood, e.g. 10-35 (or
  simply expressed as -35).

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Comparing GOF scores with Subjective Assessments

• 3 important cues of intelligibility are
• hesitation time
• speech rate
• a phoneme-by-phoneme comparison of what the
  speaker intended to say and what the listener
  actually heard.

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Calculating Phonetic Convergence
Phoneme comparison of intended and perceived
message You have to pay (for a mildly
dysarthric speaker)
Intended /j/ /u/ /h/ /æ/ /v/ /t/ /u/ /p/ /e/
Heard /j/ /u/ /h/ /æ/ /v/ /d/ /u/ /b/ /a?/
Convergence 1 1 1 1 1 0 1 0 0
Word Level Deletion -1 -1 -1 -1 -1 -1 -1 -1 -1
Overall Convergence 5 out of a possible 9 0.56 (56) 5 out of a possible 9 0.56 (56) 5 out of a possible 9 0.56 (56) 5 out of a possible 9 0.56 (56) 5 out of a possible 9 0.56 (56) 5 out of a possible 9 0.56 (56) 5 out of a possible 9 0.56 (56) 5 out of a possible 9 0.56 (56) 5 out of a possible 9 0.56 (56)
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Phonetic convergence
Hesitation
Mild, Moderate, Severe
Mild, Moderate, Severe
Speech rate
Speech rates correlation with intelligibility is
not as good as hesitation time or phonetic
convergence, so we derive a Perceptual
Intelligibility Index (PII) based on the Phonetic
Convergence score weighted by a hesitation time
coefficient
Mild, Moderate, Severe
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How well do automated GOF scores correlate with
Perceptual intelligibility index?
Speaker Phon. Convergence Hesitation Time coefficient Sentence PII Score Avg. GOF Score
Mild 0.95 0.91 0.86 -34
Moderate 0.27 0.15 0.11 -61
Severe 0.20 0.19 0.04 -85
Correlation between GOF scores and PII scores
0.72
Automated scores of goodness of fit measures
generated by HMMs could be a valid and consistent
intelligibility measure
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Summary

• FDA 2
• Analyses each parameter of speech
• Enables clinician to find cause of reduced
  intelligibility, guiding treatment
• Assists with diagnosis of dysarthria type and
  neurological impairment
• Excludes redundant tests
• Uses non-ambiguous descriptors
• Has inter and intra-rater reliability
• Large corpus of words and sentences controlled
  for linguistic and phonetic parameters for
  intelligibility sections
• Word and sentence cards provided

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Summary

• Computerised FDA
• Provides training test for new users
• Automatically produces profile and stores
  information
• Increases objectivity of measures
• Provides visual feedback of performance and
  improvements to patient
• Seeks to automate measurement of intelligibility
  leading to increased consistency

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• Thank you !