The encoding of speech modes in motor speech disorders: whispered versus normal speech in apraxia of speech and hypokinetic dysarthria

Figures & data

Table 1. Participants’ demographic information and severity.

Participant	MSD subtype	Age	Gender	Mean CP score	ASRS score	Onset of disorder
P1	AoS	54	F	4.8	8	2012
P2	AoS	53	M	6.8	39	2022
P3	AoS	29	F	2.8	29	2019
P4	AoS	35	M	1.8	11	2020
P5	AoS	51	M	5.5	40	2021
P6	AoS	53	F	6.0	12	2017
P7	AoS	56	M	1.6	20	2023
P8	AoS	56	F	6.0	14	2022
P9	AoS	37	F	4.2	13	2018
P10	AoS	70	M	8.4	30	2017
P11	HD	69	M	3.0	NA	NA
P12	HD	84	M	5.2	NA	2011
P13	HD	54	F	3.6	NA	NA
P14	HD	82	M	7.6	NA	2009
P15	HD	52	M	5.4	NA	2002
P16	HD	70	M	6.0	NA	NA
P17	HD	80	M	4.6	NA	2002
P18	HD	65	F	4.6	NA	2015
P19	HD	69	M	3.6	NA	2017
P20	HD	78	M	4.2	NA	2017

Table 2. Examples of three matched triplets of pseudo-words.

CV-legal	CCV-legal	CCV-illegal
bɛsi/bɛmɛ	blɛsi/blɛmɛ	bdɛsi/bdɛmɛ
dimɛ/dimi	dʁimɛ/dʁimi	dvimɛ/dvimi
gomi/goʁe	gʁomi/gʁoʁe	gvomi/gvoʁe

Figure 1. Experimental design of the delayed production task (either in the normal or whispered condition).

Figure 2. Mean accuracy (proportion of correct production) by groups, by structure, by speech mode.

Table 3. Results from the generalised mixed models on accuracy.

A. Model on accuracy, AoS_vs_CNTRL-AoS: glmer(acc~(Mode +Typesyll +Group+Group:Typesyll) + (1|subject)+(1|triplets), data = database, family = ”binomial”)
	X^2		df		p
Mode	0.02		1		0.89
Group	16.87		1		<0.001
Typesyll	213.11		2		<0.001
Group*Typesyll	7.34		2		0.03
Contrasts (intercept: illegal CCV)	Estimate	SE		z-value	p
Legal_CCV	2.46	0.52		4.75	<0.001
CV	1.88	0.41		4.60	<0.001
Emmeans Group*Typesyll (CNTRL-AoS vs AoS)	Estimate	SE	df	Z ratio	p
Illegal_CCV	2.26	0.52	Inf	4.38	<0.001
Legal_CCV	2.41	0.70	Inf	3.43	<0.001
CV	0.99	0.64	Inf	1.54	0.12
B. Model on accuracy, HD_vs_CNTRL-HD: glmer(acc~(Mode +Typesyll+Order+Group+Order) + (1|subject)+(1|triplets), data = database, family = ”binomial”)
	X^2		df		p
Mode	0.13		1		0.72
Group	10.17		1		0.001
Typesyll	56.44		2		<0.001
Order	4.07		1		0.04
Contrasts (intercept: illegal CCV)	Estimate	SE		z-value	p
Legal_CCV	1.0	0.20		4.96	<0.001
CV	1.61	0.23		6.97	<0.001
C. Model on accuracy, AoS_vs_HD: glmer(acc~(Mode +Typesyll+Order+Group+Group:Typesyll) + (1|subject)+(1|triplets), data = database, family = ”binomial”)
	X^2		df		p
Mode	0.08		1		0.78
Group	1.96		1		0.16
Typesyll	215.32		2		<0.001
Order	10.19		1		0.001
Group*Typesyll	22.39		2		<0.001
Contrasts (intercept: illegal CCV)	Estimate	SE		z-value	p
Legal_CCV	2.34	0.22		10.57	<0.001
CV	3.18	0.27		11.94	<0.001
Emmeans Group*Typesyll (AoS vs HD)	Estimate	SE	df	Z ratio	p
Illegal_CCV	−1.49	0.67	Inf	−2.22	0.03
Legal_CCV	−0.22	0.70	Inf	−0.32	0.75
CV	−0.11	0.72	Inf	−0.72	0.88

Figure 3. Response latencies by group of participants, by speech mode.

Table 4. Results from the linear mixed model on response latencies.

A. Model on RTs, AoS_vs_CNTRL-AoS: lmer(rts~(Mode*Group+Order) + (1|subject)+(1|triplets), data = database, REML = FALSE)
	Sum of square	Mean square	Num/DenDF	F	p
Mode	264712	264712	1/1215.70	9.61	0.002
Group	385153	385153	1/19.96	13.98	0.001
Order	617574	617574	1/1220.76	22.41	<0.001
Mode*Group	243224	243224	1/1216.28	8.83	0.003
Emmeans Mode*Group (whispered-normal)	estimate	SE	df	Z ratio	p
Group = CNTRL-AoS	1.28	12.90	1219	0.10	0.92
Group = AoS	57.71	14.0	1219	4.12	<0.001
B. Model on RTs, HD_vs_CNTRL-HD: lmer(rts~(Mode*Group+Order) + (1|subject)+(1|triplets), data = database, REML = FALSE)
	Sum of square	Mean square	Num/DenDF	F	p
Mode	25399	25399	1/1248.74	1.52	0.22
Group	12189	12189	1/19.89	0.73	0.40
Order	273481	273481	1/1252.43	16.32	<0.001
Mode*Group	216188	216188	1/1248.81	12.90	<0.001
Emmeans Mode*Group (whispered-normal)	estimate	SE	df	Z ratio	p
Group = CNTRL-HD	17.2	9.91	1251	1.73	0.08
Group = HD	−35.1	10.69	1251	−3.29	0.001
C. Model on RTs, AoS_vs_HD: lmer(rts~(Mode*Group+Order) + (1|subject)+(1|triplets), data = database, REML = FALSE)
	Sum of square	Mean square	Num/DenDF	F	p
Mode	36763	36763	1/1133.69	1.21	0.27
Group	174837	174837	1/19.97	5.75	0.03
Order	370553	370553	1/1136.74	12.18	<0.001
Mode*Group	626349	626349	1/1133.33	20.59	<0.001
Emmeans Mode*Group (whispered-normal)	estimate	SE	df	Z ratio	p
Group = AoS	57.9	14.7	1136	3.94	<0.001
Group = HD	−35.4	14.7	1137	−2.46	0.01

Figure 4. Acoustic duration by group of participants, by speech mode.

Table 5. Results from the linear mixed model on acoustic durations.

A. Model on duration, AoS_vs_CNTRL-AoS: lmer(duration~(Mode+Group) + (1|subject)+(1|triplets), data = database, REML = FALSE)
	Sum of square	Mean square	Num/DenDF	F	p
Mode	483616	483616	1/1130.54	63.74	<0.001
Group	33932	33932	1/19.84	4.47	0.05
B. Model on duration, HD_vs_CNTRL-HD: lmer(duration~(Mode+Group) + (1|subject)+(1|triplets), data = database, REML = FALSE)
	Sum of square	Mean square	Num/DenDF	F	p
Mode	159246	159246	1/1237.03	43.13	<0.001
Group	533	533	1/19.88	0.14	0.71
Order	36018	36018	1/1237.29	9.75	0.002
Mode*Group	13148	13148	1/1237.03	3.56	0.06
C. Model on duration, AoS_vs_HD: lmer(duration~(Mode+Group) + (1|subject)+(1|triplets), data = database, REML = FALSE)
	Sum of square	Mean square	Num/DenDF	F	p
Mode	288365	288365	1/1100.53	34.86	<0.001
Group	4793	4793	1/19.91	0.58	0.46
Order	44376	44376	1/1101.02	5.36	0.02
Mode*Group	69894	69894	1/1100.46	8.45	0.003
Emmeans Mode*Group (whispered-normal)	Estimate	SE	df	t	p
Group = AoS	48.2	7.89	1104	6.10	<0.001
Group = HD	16.4	7.59	1103	2.16	0.03

Data availability statement

The datasets generated during and/or analysed during the current study are available in the [Yareta institutional open science archive (https://yareta.unige.ch/home)] repository’. The link for the material is the following: https://yareta.unige.ch/archives/6dde9793-1bb4-4e85-8a98-61eed2b43ab1.