Frenchay Dysarthria
Assessment: What’s new?
Rebecca Palmer, Pam Enderby,
James Carmichael
Topics
 Original FDA overview
 Advantages and disadvantages of this assessment
 FDA 2 – new aspects
 Computerised FDA
 Demonstration
 Current work on automated intelligibility testing
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
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
Structure of FDA
 Reflexes
 Cough, swallow, dribble/drool
 Respiration
 Lips
 At rest, in speech
 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
Procedure
 Ask patient to carry out a task
 Rate ability of each parameter using a 9
point scale – 5 descriptors + ½ marks
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
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)
FDA 2
 Authors: Pam
Enderby & Rebecca
Palmer
 2008
 Aim: To address
theoretical and
practical issues
identified in reviews of
the first edition
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 didn’t assist diagnosis
Improvements 2

Improved reliability of descriptors

Inter-rater reliability testing between
experienced users of the FDA showed that
some descriptors were interpreted differently.

a)
e)
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)
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
Inter and intra judge reliability
Judge
1
1
2
3
4
5
2
0.77
0.92
3
0.56
0.65
0.72
4
5
0.67
0.38
0.60
0.52
0.51
0.49
0.79
0.73
6
0.66
0.72
0.70
0.49
0.56
6
0.76
0.76
Criteria for interpretation of reliability coefficients for ordinal measures (Landis & Koch,
1977):
<0 = 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)
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:
‘Kenneth’s dog took ten tiny ducks today’
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 >10 per million to control for any
effects of word frequency on intelligibility
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’
Availability
 FDA 2 available now from Pro-ed
 Only in English!
Computerised FDA
 James Carmichael produced computer
version
 Demonstration
Planned additions to CFDA
Automation of intelligibility testing – modelling the naiive listener
 If the learning effect alters a listener’s
perception of a particular individual’s
speaking style, is that listener’s 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)?
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
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).
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.
Calculating Phonetic Convergence
Phoneme comparison of intended and perceived message: “You have to pay”
(for a mildly dysarthric speaker)
/j/
/u:/
/h/
/æ/
/v/
/t/
/u:/
/p/
/e/
Heard
/j/
/u:/
/h/
/æ/
/v/
/d/
/u:/
/b/
/aι/
Convergence
1
1
1
1
1
0
1
0
0
Intended
Word Level
Deletion
Overall
Convergence
-1
5 out of a possible 9 = 0.56 (56%)
1
1
0.9
0
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
L1
L1
Mild, Moderate,
Severe
Listener
5
0
L
L
1
5
DS1 Isolated Words
DS1 Sentences s
L2
0
DS2 Isolated Words
DS2 Sentences
DS3 Isolated Words
DS3 Sentences
Speech rate
1
0.5
Speech Rate (Rel. to Norm)
Hesitation
Hesitation (Rel. to Norm)
Phonetic Convergence
Phonetic convergence
0
-0.5
-1
-1.5
-2
-2.5
-3
-3.5
-4
Mild, Moderate, Severe
DS1 Isolated Words
DS1 Sentences
DS2 Isolated Words
DS2 Sentences
DS3 Isolated Words
DS3 Sentences
-1
-2
-3
-4
-5
-6
-7
L10
L15
Mild, Moderate,
Severe
Listener
L
L
5
1
DS1 Isolated Words
DS1 Sentences s
DS2 Isolated Words
DS2 Sentences
DS3 Isolated Words
DS3 Sentences
L20
Speech rate’s 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
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
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
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
Thank you !