A Six

A Bocheux

F Charra

F Mathevet

D Kreher
email: david.kreher@upmc.fr

A-J Attias

2D self-assembly of phenylene-vinylene tectons at the liquid-HOPG interface: from the concentration and alkyl chains length effects to host-guest systems

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

2D self-assembly of phenylene-vinylene tectons at the liquid-HOPG interface: from the concentration and alkyl chains length effects to host-guest systems

A Six, A Bocheux, Fabrice Charra, F Mathevet, David Kreher, André-Jean Attias

To cite this version:

A Six, A Bocheux, Fabrice Charra, F Mathevet, David Kreher, et al.. 2D self-assembly of phenylenevinylene tectons at the liquid-HOPG interface: from the concentration and alkyl chains length effects to host-guest systems. Nanotechnology, 2016, 28, pp.025602 10.1088/1361-6528/28/2/025602. hal-

Introduction

Surface noncovalent nanostructuration by supramolecular self-assembly of organic building blocks (so-called "tectons") has attracted much interest for several years [1], in view of new potential applications in nanoscience such as molecular electronics [2], photonics or nanomechanical devices [3]. In particular, the formation of a two-dimensional (2D) self-assembled monolayer at the interface between an atomically-flat substrate and a solution of molecular building blocks offers a lot of flexibility and ease of fabrication, together with the possibility of matter exchanges with the liquid environment when working on such surface-confined nanomaterials [4]. In addition to the control of molecule positioning leading to complex nanopatterns, the control of surface properties has also been addressed more recently [5]. In that respect, nanoporous networks with specific properties and presenting selective adsorption and/or surface diffusion of guest molecules were developed [6]. Owing to the host properties of these networks towards organic guests, they allowed for addressing various interesting aspects such as molecular dynamics, selection, recognition, and immobilization, hence leading to functional surfaces [7]. Moreover, these 2D surface-confined nanoporous molecular networks systems can thereby act as templates for the construction of multicomponent 2D hierarchical assemblies [8], most of them giving an isotropic network as obtained from trifunctional molecular building blocks. Among them, we previously developed a strategy to form "on demand" non-covalent self-assemblies with predetermined 2D topologies on sp2-hybridized carbon-based substrates at the liquid-solid interface at room temperature. This method dealt with 2D self-assembly of building blocks designed so as to enable intermolecular interdigitation of alkyl side-chains thus forming noncovalent bonding units (so-called "molecular clips") [9]. Following this principle, we first designed a family of trifunctional molecules based on a symmetrical tristilbene core substituted by three molecular clips leading consequently to the formation of isotropic honeycomb-like nanoporous monolayers. We demonstrated its interest as a molecular sieve [10] and determined the thermodynamic conditions of stability of such a nanoporous phase [11,12].

More recently, taking advantage of this novel functional unit, we obtained 2D patterns of well-defined supramolecular polymer-like chains, formed by a bifunctional tecton, which can self-organize into anisotropic nanoporous networks [13]. However, the precise study and description of this latter tecton self-assembly on HOPG was not investigated so far. In particular, its ability to trap or not guest molecules and the influence of parameters such as concentration on the experimental procedure has not been reported yet.

Here, we first report the synthesis of a series of tectons BS-Cn varying in alkyl chains length (figure 1, n = 10, 12 and 14) (BS-Cn). We then describe the influence of the concentration on the self-assembly of the tecton BS-C10, in order to find the optimal experimental parameters to favor the formation of an anisotropic nanoporous network. We demonstrate that by varying the alkyl chain length it is possible to finely tune the anisotropic network lattice parameters. Finally, preliminary results regarding the ability of this nanoporous network to host third-party molecules are presented.

Results and discussion

Synthesis of "bone-shape" tectons BS-Cn

All the BS-Cn tectons were prepared as shown in figure 1, by adapting a procedure described in the literature [15]. First, compounds Clip-Cn were synthesized with various alkyl chain length (n = 10, 12, and 14). Then, two equivalents of Clip-Cn underwent a Wittig-Horner reaction with one equivalent of 2,5-dimethylterephthalaldehyde (dialdehyde on figure 1) obtained itself from the commercially available 1,4-bis(chloromethyl)-2,5-dimethylbenzene. The structures of target tectons BS-Cn were confirmed by proton and carbon NMR spectroscopies, elemental analysis, and mass spectrometry (see appendix A).

Photophysical properties in solution of tectons BS-Cn

Absorption In addition, molar extinction coefficients () have been determined by varying the solution concentrations (table 1).

Compound Molar extinction coefficient at 324 nm

BS-C10 111545 L.mol -1 .cm -1 Clip-C10 39284 L.mol -1 .cm -1 TriPPV 26792 L.mol -1 .cm -1
The sum of TriPPV molar extinction coefficient and twice that of the Clip-C10 gives ε = 105360 L.mol -1 .cm -1 , a value very close to the BS-C10 molar extinction coefficient: ε = 111545 L.mol -1 .cm -1 . The difference between these two values can be explained by an increased conjugation in the BS-C10 compound, compared to Clip-C10 and TriPPV compounds. This result proves that BS-C10 absorption is due to the absorption of both Clip-C10 units and the central core.

Similar results have been obtained for BS-C12 and BS-C14 molecules demonstrating that there is no influence of the side alkyl chains length on the spectral shape. Only the molar extinction coefficient at 324 nm increases with the side alkyl chain length, to reach around ε = 123918 L.mol -1 .cm -1 and ε = 135064 L.mol -1 .cm -1 respectively for BS-C12 and BS-C14. Such small increase with longer alkyl chains could be due to the increased mass of the alkyl chains, which could dump the skeleton fluctuations a little more efficiently.

Emission spectra have also been recorded, by exciting the compounds at their absorption maximum, e.g. 324 nm for BS-Cn molecule, 308 nm for corresponding Clip-Cn, and 353 nm for TriPPV molecule. All the spectra are very similar, whatever the side alkyl chain length. As an example with BS-C10 (figure 3), emission maxima at 419 nm and 442 nm are observed. This structure is typical of central TriPPV emission, even though the spectra are switched toward higher wavelengths, due to an increased conjugation. Excitation spectra have also been recorded for each compound at their emission maxima: in all cases, they were similar to the absorption spectra of the corresponding molecules. Therefore, in each case, there is only one specie at the excited state. To conclude, side alkyl chains variation does not affect photophysical properties of BS-Cn compounds.

Self-assembly properties of BS-C10at the liquid-solid interface: effect of the concentration

First, the self-assembly properties of compound BS-C10 were studied by STM at the solution-solid interface at different concentrations. The solutions of BS-C10 were deposited just after cleaving the substrate and were then imaged by STM at room temperature. Through this procedure, the growth is controlled by the kinetics rather than the thermodynamics and it thus leads to the formation of various types of networks and not only that which is thermodynamically the most stable. This coexistence is explained by an initial nucleation-growth phenomenon, followed by a kinetic blockade [12]. The hypothesis is that during nucleation, molecules nucleate on the HOPG surface according to the "molecular clip" process and it is at this moment that the configuration is determined. Actually, for 3.10 -4 mol.L -1 solutions, many different networks were obtained with surface densities ranging from 0.13 molecule/nm² (nanoporous network) to 0.21 molecule/nm² (most compact network). For a concentration reduced to 1.10 -4 mol.L -1 solutions the majority network is the nanoporous one (figure 4 (a) and (b)). On the STM images the conjugated moieties appear much brighter than the aliphatic parts. The lattice parameters of the nanoporous network shown on figure 4 are a = 3.9 nm, b = 2.1 nm and α = 65° and the density is 7.4 nm² per molecule, which is higher than the density of the compact network (5.6 nm² per molecule). Moreover, previous work has led to a reproducible procedure leading to the nanoporous network [12], therefore we will focus on the study of this network only. Similar concentrationdependent self-assemblies have been reported in literature [14], and an expression of the critical concentration and a procedure leading to the thermodynamically stable lattice have been derived for the self-assembly of trifunctional tristilbenes (TSB) [12].

Self-assembly properties of BS-Cn at the liquid-solid interface: influence of alkyl chain length

In the case of the nanoporous network, the exclusive formation of which is well-controlled [12], we investigated the possibility to finely tune the distance between cavities, as it constitutes a prerequisite toward the control and exploitation of such anisotropic network. To achieve that, first we developed and synthesized several building blocks with various peripheral alkyl chain lengths, more precisely bifunctionnal "bone-shape tectons" BS-C10, BS-C12 and BS-C14 which are presented on figure 5. Second, we managed to control and realize a series of nanoporous networks by self-assembly of these tectons on HOPG. In these conditions, the self-assemblies obtained are stable and self-replicated on distances of at least 100 nm. Moreover, this nanoporous network is obtained with the same self-assembly pattern for each alkyl chain length, as seen on models. Only the lattice parameter "a", i.e. the longitudinal distance between cavities, increases with the alkyl chain length, whereas the cavity size remains the same, as evidenced with the lattice parameters reported in table 2. As a consequence, obtaining the nanoporous network remains as easy for longer alkyl side chains, in contrast with TSB35 for which the cavity size increases with alkyl chains which restrains the stability domain for the nanoporous network. Therefore, the "molecular clip" function is effective for alkyl chain length ranging at least from ten to fourteen carbons. Note that, since the vector a is nearly parallel to the alkyl chains (i.e. the 110 direction of HOPG), the variation of the angle α is not significant and the decrease of the angle predicted by the theory is lower than the uncertainty on the measurements.

In addition, a closer look on some STM images reveals a slight variant in alkyl chain packings. More specifically two configurations can be discerned. The first one can be called "regular" (figure 6 (a)), which is the template generally proposed, and a second one corresponding to a reconstruction

( 0 8 12 + 𝑛/2 +1
) with respect to HOPG (n is the alkyl chain length) is named "gauche" (figure 6 On most of the STM images, the regular configuration is observed,. Experimentally the gauche configuration appears as a defect. More precisely, it appears at the boundaries of domains with translational mismatch accidentally fitting the gauche configuration. At places where such a domain boundary crosses a lamella, a local breaking of the periodicity along the lamella is formed as shown on figure 7.

Co-assembly structures of BS-C10 and guest molecules on HOPG surface

Nanoporous networks are likely to host third-party compounds in their cavities. We investigated the ability of a BS-C10 self-assembly to trap guest molecules. Three guest candidates have been chosen, benzo[rst]pentaphene (BPP), pentacene and hexabenzocoronene (HBC) (figure 8), the dimensions of which fitting the cavity size. For BPP and pentacene, even one hour after their addition, only few successful insertions have been observed by STM (figures 8 (a) and (b)): this could be explained by the fact that they are either too small to be adsorbed inside the cavities or too small again to give a very easy STM signature enabling its differentiation from the host self-assembly (their motion inside the cavity leading to a blurred poorly contrasted STM response). Indeed, the low interaction surface between the molecules and the substrate, which induce a low adsorption enthalpy, and their high entropy in the solution could increase their desorption, compared to bigger molecules. The same deposition protocol has been carried out at various concentrations and led to the same conclusion.

Oppositely, HBC remained sequestered inside the network and all the cavities appeared filled after a waiting time of one hour, as seen on figure 8 (c). This can be explained by the fact that the shape and size of HBC are closer to the shape and size of the cavities. At lower concentrations these molecules can still be trapped again in the cavities, but not all of the latter are filled (sub-stoichiometry conditions). Then, guest molecules dynamics appears as a blinking of the corresponding spots in successive images. In most cases the appearance of a spot in one cavity is concomitant with the disappearance of another one in its neighborhood, as illustrated on figure 9.

The circle shows a disappearing HBC molecule and the arrow shows uniformly distributed stripes parallel to the fast-scan direction. Those stripes correspond to occupation changes occurring in the course of cavity imaging and evidence that the guest molecules are moving when working in such substoichiometry conditions. In addition, this behavior was observed whatever the length of the peripheral alkoxy chains, which means that in this case (contrary to our precedent studies with trifunctional TSB entities) the chain length parameter don't lead to a modification of the cavities and thus of the molecular sieve properties: increasing the chain length is just a way we can play with in order to tune in a control way the distance between these cavities.

Conclusion

In summary, a series of new phenylene-vinylene tectons substituted with peripheral alkyl chains with various lengths have been synthesized. The supramolecular self-assembly properties at liquid/solid interface have been investigated by STM. Changes in concentration led to the formation of various networks, several compact ones and two nanoporous ones. Fine tuning of the peripheral alkyl chain length led as well to the variation of the distance between cavities on one axis only. This distance varies step-by-step, while preserving the cavity size and the detailed features of the HOPG-induced alkyl chain interdigitation. Finally, the nanoporous network can be used as a host-guest system with selectivity toward guest molecules depending on their sizes and shapes, therefore it could be used as a molecular sieve.

Figure 1 .

 1 Figure 1. Synthesis of the BS-Cn compounds, differing from the alkyl side chains -CnH2n+1.

  and emission spectra in solution of compounds Clip-Cn, triPPV, and BS-Cn have been recorded. In absorption, all the spectra of tectons BS-Cn have the same behavior and a typical UVvisible spectrum is reported in figure 2 (compound BS-C10). It is almost the superposition of the spectra of the corresponding Clip-C10 (λmax = 308 nm) and the central stilbenes model compound TriPPV (λmax = 353 nm) (figure 2).

Figure 2 .

 2 Figure 2. Absorption spectra of BS-C10, Clip-C10 and TriPPV compounds.

Figure 3 .

 3 Figure 3. Emission spectra for BS-C10, Clip-C10 and TriPPV compounds.

Figure 4 .

 4 Figure 4. STM images and molecular model of adsorption on HOPG at the HOPG-phenyloctane interface. a),b) STM images of BS-C10 self-assembly for a deposition at a) 3.10 -4 mol.L -1 (Circle: the most compact network, Rectangle: nanoporous network) (V = -930 mV, I = 40 pA, 100x100 nm²), b) 1.10 -4 mol.L -1 (nanoporous network only) (V = -870 mV, I = 30 pA, 20x20 nm²), c) Molecular models of one unit cell of the monolayer adsorbed on HOPG for the nanoporous network.

Figure 5 .

 5 Figure 5. STM images and molecular model of adsorption on HOPG at the HOPG-phenyloctane interface. a) Large-scale STM image of a monolayer domain of compound BS-C10 (V = -650 mV, I = 16 pA, 255x255 nm²) ; b) Close-up STM image of BS-C10 self-assembly (V = -870 mV, I = 30 pA, 20x20 nm²), c) Molecular model associated to compound BS-C10, d) Largescale STM image of a monolayer domain of compound BS-C12 (V = -1150 mV, I = 11 pA, 70x70 nm²) ; e) Close-up STM image of BS-C12 self-assembly (V = -1150 mV, I = 12 pA, 22x22 nm²), f) Molecular model associated to compound BS-C12, g) Large-scale STM image of a monolayer domain of compound BS-C14 (V = -650 mV, I = 11 pA, 80x80 nm²) ; h) Close-up STM image of BS-C14 self-assembly (V = -680 mV, I = 12 pA, 15x15 nm²), i) Molecular model associated to compound BS-C14.

  (b)),which is encountered sometimes. It corresponds to an inversion of the clips, that is to a shift by a vector (0 4) of an entire lamella. This shift being parallel to a lattice vector (b = (0 8)) this variant preserves the unit cell area and the surface molecular density. The most visible change is the decrease of the lattice angle , e.g. from 70° (gauche) to 57° (regular) in the case n = 10.

Figure 6 .

 6 Figure 6. Molecular model of nanoporous network in both configurations: a) Regular, b) Gauche.

Figure 7 .

 7 Figure 7. STM image of compound BS-C14 with both configurations observed: gauche (left rectangle), regular (right rectangle) and local defect resulting from a domain boundary crossing the lamella (white circle).

Figure 8 .

 8 Figure 8. a) Molecular structures of the guest molecules BPP, pentacene and HBC, b-d) STM images of the self-assembled monolayer of the matrix formed through self-assembly of BS-C10 on HOPG, after deposition of b) BPP (V = -970 mV, I = 25 pA, 24x24 nm²), c) pentacene (V = -1150 mV, I = 40 pA, 40x40 nm²), or d) HBC (V = -1425 mV, I = 25 pA, 79x79 nm²).Red circles highlight guest insertion.

Figure 9 .

 9 Figure 9. Two successive STM images which show an occupation state change during the imaging (arrow) and the disappearance of a HBC molecule (circle). V = -1000 mV, I = 30 pA, 25x25 nm².

Table 1 . Molar extinction coefficients (ε) of BS-C10, Clip-C10 and TriPPV compounds.

 1

Table 2 . Analysis of molecular lattice evolution with increasing alkyl chain lengths from C10H21 to C14H29. The reconstruction with respect to HOPG is given.

 2

	Alkyl chain length Reconstruction Lattice parameter a Lattice parameter b	Angle α
				Theory (measure)	Theory (measure) Theory (measure)
	10 carbons 12 carbons 14 carbons	( 17 -3 0 8 ( 0 8 18 -3 ( 0 8 19 -3	) ) )	4.0 nm (3.9) 4.2 nm (4.1) 4.4 nm (4.3)	2.0 nm (2.1) 2.0 nm (2.1) 2.0 nm (2.1)	70° (65) 69° (69) 68°

Acknowledgments

We gratefully acknowledge financial support by CNano Ile de France. The authors also thank J Li Master student for technical assistance with UV and fluorescence spectra.

Appendix A

Tetrahydrofurane (THF), Toluene and Dichloromethane (CH2Cl2) used in synthesis come from a fountain. All other solvents were purchased from commercial suppliers and used without further purification. Chromatographic separations were carried out with a flash chromatographic instrument (Grace, Reveleris) and with silica gel (40 μm, Grace). Structural information of materials was obtained by 1 H NMR and 13 C spectroscopy. Spectra were recorded at 200 MHz, proton chemical shifts (δ) are reported in ppm and referenced to the residual solvent signal: CDCl3 (7.26). 13 C NMR spectra were recorded at 200 MHz and δ referenced to the residual solvent signal: CDCl3 (77.16).

The BS-Cn tectons have been synthesized and purified by adapting a general procedure for the Wittig-Hörner reaction described in the literature [15], using the corresponding "molecular clip" compounds Clip-Cn. To a solution of Clip-Cn (2.2eq) in dry THF under argon atmosphere was added tBuOK (1.5eq). The mixture was stirred for 15mn at 0°C then the dialdehyde (1eq) was added. The reaction was stirred for 2h at 0°C. The resulting mixture was quenched with water and concentrated to remove the THF, then extracted with CH2Cl2 and washed with brine. The combined organic layers were dried over MgSO4, filtered and concentrated under vacuum. The residue was purified by flash chromatography (1:1 CH2Cl2:PE) to yield the pure target material BS-Cn. 3,3',3'',1'E,1''E,1'E)-(2,5-dimethyl-1,4-phenylene)bis(ethene-2,1diyl))bis(benzene-5,3,1-triyl))tetrakis(ethene-2,1-diyl))tetrakis((decyloxy)benzene) (BS-C10) Yellow solid. Yield: 200mg, 27%. Rf = 0.59 (1:1 CH2Cl2:PE). 1 H NMR (250 MHz, CD3Cl) δ: 0.87 (t, J = 6.7 Hz, 12H, H27), 1.28 (m, 56H, H20-26), 1.78 (m, 8H, H19), 2.49 (s, 6H, H28), 3.98 (t, J = 6.5 Hz, 8H, H18), 6.81 (m, 4H, H15), 7.10 (d, J = 16.0 Hz, 2H, H5), 7.11 (m, 8H, H10,11), 7.17 (s, 8H, H13,17), 7.27 (t, J = 7.7 Hz, 4H, H14), 7.42 (d, J = 16.0 Hz, 2H, H4), 7.50 (s, 2H, H2), 7.56 (m, 6H, H7,9).

3,3',3'',3'''-((1E,1'E,1''E,1'''E)-(((1E,1'E)-(2,5-dimethyl-1,4-phenylene)bis(ethene-2,1diyl))bis(benzene-5,3,1-triyl))tetrakis(ethene-2,1-diyl))tetrakis((dodecyloxy)benzene) (BS-C12)

Yellow solid. Yield: 59mg, 18%. Rf = 0.60 (1:1 CH2Cl2:PE). 1 H NMR (200MHz, CDCl3) δ: 0.87 (t, J = 6.7 Hz, 12H, H29), 1.28 (m, 72H, H20-28), 1.81 (m, 8H, H19), 2.50 (s, 6H, H30), 4.00 (t, J = 6.5 Hz, 8H, H18), 6.83 (m, 4H, H15), 7.11 (d, J = 16.0 Hz, 2H, H5), 7.15 (m, 8H, H10-11), 7.18 (s, 8H, H13,17), 7.28 (t, J = 7.7 Hz, 4H, H14), 7.44 (d, J = 16.0 Hz, 2H, H4), 7.48 (s, 2H, H2), 7.55 (m, 6H, H7,9).

3,3',3'',3'''-((1E,1'E,1''E,1'''E)-(((1E,1'E)-(2,5-dimethyl-1,4-phenylene)bis(ethene-2,1diyl))bis(benzene-5,3,1-triyl))tetrakis(ethene-2,1-diyl))tetrakis((tetradecyloxy)benzene) (BS-C14)

Yellow solid. Yield: 135mg, 22%. Rf 0.57 (1:1 CH2Cl2:PE). 1 H NMR (200MHz, CDCl3) δ: 0.88 (t, J = 6.7 Hz, 12H, H31), 1.27 (m, 88H, H20-30), 1.82 (m, 8H, H19), 2.52 (s, 6H, H32), 4.02 (t, J = 6.5 Hz, 8H, H18), 6.84 (m, 4H, H15), 7.11 (d, J = 16.0 Hz, 2H, H5), 7.15 (m, 8H, H10,11), 7.18 (s, 8H, H13,17), 7.28 (t, J = 7.7 Hz, 4H, H14), 7.44 (d, J = 16.0 Hz, 2H, H4), 7.48 (s, 2H, H2), 7.56 (m, 6H, H7,9).

Appendix B

UV-visible spectra were recorded with a ThermoFisher evolution UV/Vis spectrophotometer coupled with a thermal controller. Cells were in quartz with two faces (thickness = 1cm).

UV-Visible spectra have been recorded in CHCl3 at room temperature, for concentrations between 10 -6 mol.L -1 and 10 -5 mol.L -1 . Molar extinction coefficients have been calculated thanks to Beer-Lambert law: A = l.ε.c. This law is applicable in the concentration range used, so we can consider that there are neither dimerisations nor oligomerisations of the molecules.

Emission and excitation spectra have been recorded in CHCl3 at room temperature, for concentrations ranging from 10 -6 mol.L -1 to 10 -5 mol.L -1 . Emission spectra have been obtained by exciting the compounds at their absorption maximum, e.g. 324 nm for BS-Cn molecules, 308 nm for Clip-Cn molecules, and 353 nm for TriPPV molecule.

Appendix C

STM images were acquired at room temperature with a homemade digital system. The fast-scan axis was kept perpendicular to the sample slope. All images were obtained in the height mode, i.e. with real-time current regulation. Images acquired simultaneously in both fast scan directions were systematically recorded and compared. All images were corrected for the drift of the instrument, by combining two successive images with downward and upward slow-scan directions, using especially developed image cross-correlation software called Imago. The solvent for the self-assembly was 1-phenyloctane (98%, Aldrich), which is well suited for in situ scanning tunneling microscopy (STM) because of its low conductivity, slow volatility and high dielectric rigidity and also avoids the coadsorption often observed with linear alkanes. The substrate was a freshly cleaved HOPG (SPI, grade 2) and the tips were mechanically formed from a 250mm Pt/Ir wire (Pt80Ir20, Goodfellow). The freshly-cleaved sample and tip quality was systematically checked by STM observation of HOPG atomic network prior to molecular deposition. The monolayers were formed by immersing the STM junction in a droplet (ca. 5 μL) of a solution immediately after observation of HOPG atomic network. Imaging was then carried out in situ at the liquid-solid interface.


Imed Laaridh
email: imed.laaridh@alumni.univ-avignon.fr

Corinne Fredouille
email: corinne.fredouille@univ-avignon.fr

Christine Meunier
email: christine.meunier@lpl-aix.fr

Automatic Anomaly Detection for Dysarthria Across Two Speech Styles: Read vs Spontaneous Speech

Keywords: Dysarthria, automatic speech processing, anomaly detection, read speech, spontaneous speech, speech disorders

Perceptive evaluation of speech disorders is still the standard method in clinical practice for the diagnosing and the following of the condition progression of patients. Such methods include different tasks such as read speech, spontaneous speech, isolated words, sustained vowels, etc. In this context, automatic speech processing tools have proven pertinence in speech quality evaluation and assistive technology-based applications. Though, a very few studies have investigated the use of automatic tools on spontaneous speech. This paper investigates the behavior of an automatic phone-based anomaly detection system when applied on read and spontaneous French dysarthric speech. The behavior of the automatic tool reveals interesting inter-pathology differences across speech styles.

Introduction

Dysarthria is a motor speech disorder resulting from neurological damages located either in the central or in the peripheral nervous system. This may lead to disturbances in any of the components involved in the speech production, including respiratory, phonatory, resonatory, articulatory and prosodic elements. Consequently, this may be reflected by weakness, spasticity, incoordination, involuntary movements, or abnormal muscle tone, depending on the location of the neurological damage. Dysarthric speech has been studied according to different axes : perceptual evaluation of speech alterations for dysarthria classification [START_REF] Darley | Differential diagnostic patterns of dysarthria[END_REF][START_REF] Duffy | Motor speech disorders: substrates, differential diagnosis and management[END_REF][START_REF] Darley | Motor speech disorders[END_REF], different speech tasks [START_REF] Van Lancker Sidtis | Dramatic effects of speech task on motor and linguistic planning in severely dysfluent parkinsonian speech[END_REF][START_REF] Kempler | Effect of speech task on intelligibility in dysarthria: a case study of parkinson's disease[END_REF], perceptual measurement of dysarthria severity, notably related to the speaker's intelligibility [START_REF] Enderby | Frenchay dysarthric assessment[END_REF][START_REF] Yorkston | Comprehensibility of dysarthric speech: implications for assessment and treatment planning[END_REF][START_REF] Hustad | The relationship between listener comprehension and intelligibility scores for speakers with dysarthria[END_REF][START_REF] Lowit | Assessment of motor speech disorders[END_REF] or articulatory or/and acoustic analysis [START_REF] Kent | Acoustic studies of dysarthric speech: Methods, progress, and potential[END_REF][START_REF] Mcauliffe | Speech production in parkinson's disease: I. an electropalatographic investigation of tongue-palate contact patterns[END_REF][START_REF] Rosen | Parametric quantitative acoustic analysis of conversation produced by speakers with dysarthria and healthy speakers[END_REF][START_REF] Green | Bulbar and speech motor assessment in als: Challenges and future directions[END_REF][START_REF] Whitfield | Articulatory-acoustic vowel space: Application to clear speech in individuals with parkinson's disease[END_REF] in order to observe and characterize the effects of dysarthria in the speech signal. These studies aim at helping clinicians in their knowledge of the speech impairment and its clinical evaluation, crucial for following the condition progression of patients in the case of treatment or/and of speech rehabilitation to enhance them. In this context, automatic speech processing approaches have been seen, very early, as potential solutions to provide objective tools to deal with speech disorders [START_REF] Ferrier | A case study of a dysarthric speaker using the dragondictate voice recognition system[END_REF][START_REF] Rosen | Automatic speech recognition and a review of its functioning with dysarthric speech[END_REF][START_REF] Carmichael | Introducing objective acoustic metrics for the Frenchay Dysarthria Assessment procedure[END_REF][START_REF] Middag | Automated intelligibility assessment of pathological speech using phonological features[END_REF][START_REF] Nuffelen | Speech technology-based assessment of phoneme intelligibility in dysarthria[END_REF][START_REF] Kim | Automatic assessment of dysarthric speech intelligibility based on selected phonetic quality features[END_REF]. In addition, they can also help people with speech disorders in their everyday life through Alternative and Augmented Communication (AAC) tools, involving automatic speech recognition for instance [START_REF] Green | Automatic speech recognition with sparse training data for dysarthric speakers[END_REF][START_REF] Parker | Automatic speech recognition and training for severely dysarthric users of assistive technology: the stardust project[END_REF][START_REF] Rudzicz | The torgo database of acoustic and articulatory speech from speakers with dysarthria[END_REF]Christensen et al., 2013a;[START_REF] Christensen | homeservice: Voice-enabled assistive technology in the home using cloud-based automatic speech recognition[END_REF]. In the literature, the set of acoustic-perceptual cues including imprecision of consonants, distorsion of vowels, slow rate, monopitch, monoloudness, hypernasality is commonly used to characterize the main disturbances of the various types of dysarthria in the speech production. But, more descriptive acoustic and phonetic analyses are still necessary to take into account the large variability in terms of speech alterations observed among people in different groups of diseases, within the same group [START_REF] Tomik | Dysarthria in amyotrophic lateral sclerosis: a review[END_REF] or across different speech styles (read speech, spontaneous speech, singing, isolated words, etc). Moreover, so that such analyses are relevant, they require a large number of people with speech disorders, a variety of diseases related to the different types of dysarthria (spastic, flaccid, ataxic, hyper-or hypokinetic, unilateral upper motor neuron, or mixed), various levels of condition progression and of severity degree in order to observe their effects on the speech production as well as the possible compensation strategies set up by speakers. Still here, automatic speech processing approaches would be of major interest in the task of focusing the attention of human experts on specific speech segments (among a large amount of speech productions) exhibiting unexpected acoustic patterns compared with a normal speech production. As reported in [START_REF] Chandola | Anomaly detection: a survey[END_REF] in a more general context, anomaly detection refers to the problem of finding patterns in data that do not conform to an expected behavior. In dysarthric speech, anomalies can refer to the unexpected acoustic patterns mentioned above and observed at different units of speech like phones for instance. In previous works [START_REF] Fredouille | Automatic detection of abnormal zones in pathological speech[END_REF]Laaridh et al., 2015a), the authors have proposed an automatic phone-based anomaly detection approach in two steps : a text-constrained phone alignment to obtain the phone segmentation and a classification of speech segments into normal and abnormal phones (anomalies). Furthermore, in [START_REF] Laaridh | Automatic speech processing for dysarthria: A study of interpathology variability[END_REF], the quality of the automatic phone alignment was studied and found to be depending on phonetic categories, pathologies and the severity of the dysarthria. In this paper, the authors investigate the impact of the speech styles on the automatic anomaly detection and classification process, focusing on read and spontaneous speech. Indeed, comparative studies on motor speech disorders have found different articulatory and voice characteristics (for instance speech rate and breath-pause positions [START_REF] Brown | Phonetic variation in dysarthric speech as a function of sampling task[END_REF]) between speech styles. Also, it could be expected that dysarthric speakers develop strategies to avoid "difficult" linguistic contexts. Such strategies could only be applied during spontaneous speech. Within this context, it is interesting to study whether our classification system is able to detect anomalies in spontaneous speech and whether it has the same behavior as when facing read dysarthric speech. This study is conducted in the context of French dysarthric speech produced by people suffering from four different pathologies. The rest of this paper is organized as follows. In section 2, both steps of the automatic anomaly detection approach are described. The corpora used in this study are presented in section 3. In section 4, the behavior of the approach facing both read and spontaneous speeches is compared and discussed. Finally, section 5 provides a conclusion and directions for future works.

Automatic Anomaly Detection

The anomaly detection approach studied here relies on two steps. The first step is a text-constrained phone alignment. The second step consists of a two class (normal and abnormal phones) supervised classification. In each class, phones are characterized by a set of features considered as relevant for the discrimination task.

Automatic Phone-based Alignment

The segmentation of speech utterances into phones is carried out thanks to an automatic text-constrained phone alignment tool. This tool takes as input the sequence of words pronounced in each utterance and a phonetized phonologically-varied lexicon of words based on a set of 37 French phones. The sequence of words comes from a manual orthographic transcription performed by a human listener following some annotation rules. For this manual transcription, inter-pausal units (IPUs) are annotated by the human listener. An IPU is defined as a pause-free unit of speech separated from another IPU by at least 250ms of silence or non-speech. The automatic alignment process is then based on a Viterbi decoding and graph-search algorithms, the core of which is the acoustic modeling of each phone, based on a Hidden Markov Model (HMM). In this work, each phone is modeled using a 3-state contextindependent HMM topology which are built using the Maximum Likelihood Estimate paradigm on the basis of about 200 hours of French radiophonic speech recordings [START_REF] Galliano | Ester phase ii evaluation campaign for the rich transcription of french broadcast news[END_REF]. In order to get speaker-dependent models, a three-iteration Maximum A Posteriori (MAP) adaptation is performed to adapt all the HMM parameters. This automatic alignment process results in a couple of start and end boundaries per phone produced in the speech recordings.

Normal And Abnormal Speech Classification

This step aims at characterizing each phone with a set of features found to be relevant for the anomaly detection task. The set of features used is mainly derived from the automatic text-constrained phone alignment outputs. For each phone p and its associated speech segment y p , the following features and acoustic scores are extracted:

• the phone duration, expressed in terms of the number of 10ms frames covered by y p ;

• the number of frames in y p for which the one-best state search among the HMM-based phone models, applied at the frame level, corresponds to those of p;

• the acoustic score of the best matching phone p , obtained by computing the scores of all the HMM phone models on segment y p ; if p is the best matching phone, the second-best matching phone is considered instead;

• the phonetic category of p ;

• the acoustic score of the second-best matching phone p ; if p is one of the two best matching phones, the third one is considered instead;

• the phonetic category of p ;

• the acoustic score of p and its rank compared to the scores of all the HMM phone models.

The classification task is based on Support Vector Machines (SVM), which have been largely applied to pattern recognition problems [START_REF] Vapnik | The Nature of Statistical Learning The-ory[END_REF] [START_REF] Scholkopf | Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond[END_REF].

Here, the SVM classification method is applied to a twoclass problem: discriminating between normal and abnormal phones (anomalies). Each phone is characterized with the set of features defined above. All the SVMs are used with a polynomial kernel.

In order to better take into account each phonetic category specificities and to refine abnormal and normal classes, different SVM models are trained by distinguishing the speech productions by gender and phonetic categories (unvoiced consonants, voiced consonants, oral vowels, nasal vowels).

The different SVM models are trained using the SVMlight tool (see [START_REF] Joachims | Making large-scale SVM learning practical[END_REF] for more information).

Corpora

The current study is based on two speech corpora. The first corpus (LSD-corpus) contains 8 dysarthric speakers and 6 control subjects. The dysarthric speakers had been diagnosed with rare Lysosomal Storage Diseases (LSD), resulting in a mixed dysarthria, and showed disparities in the Dysarthria Severity Degree (DSD). All the speakers were asked to read the same text, a French fairytale called "Le cordonnier" (The cobbler), as naturally as possible and they each recorded 3 to 6 longitudinal records approximately every six months. All the speech utterances were annotated by a human expert in order to identify acoustic anomalies at the phone level. Consequently, this corpus was involved in the estimate of the normal and abnormal models required in the classification step described in section 2.2.. Table 1 provides information on this corpus.

The second corpus, named Typaloc [START_REF] Meunier | The typaloc corpus: A collection of various dysarthric speech in read and spontaneous speech[END_REF], contains 28 dysarthric speakers and 12 control subjects. Each speaker read the same text as the LSD-corpus and made an additional spontaneous speech record. For both dysarthric and control speakers, the spontaneous speech situation was an interview conducted by a researcher/clinician in which they had to talk about their everyday life, personal history or even particular events. Unlike the first corpus in which only LSD patients were recorded, this corpus presents various diseases and types of dysarthria: Amyotrophic Lateral Sclerosis (ALS)/mixed dysarthria, Parkinson's Disease (PD)/hypokinetic dysarthria and Cerebellar Ataxia (CA)/ataxic dysarthria distributed over various DSDs. All the patient's speech recordings were evaluated perceptually by a jury of 11 experts who were asked to rate each patient on perceptual items of speech quality. Among these items, we focus on the DSD rated on a scale of 0 to 3 (0 -no dysarthria, 1 -mild, 2 -moderate, 3 -severe dysarthria). Table 2 provides information on the Typaloc corpus grouped per pathology and speech style. This includes the number of patients, the average values of the DSD and the average and (Min;Max) of recording duration (sec.) and number of phones in the automatic alignment per speaker within each population.

We can point out that the spontaneous speech productions are shorter (duration and # of phones) than the read speech productions independently of pathologies. Indeed, patients are usually less prone to talk spontaneously due, not only to their pathologies and the fatigue effect, but also to the speech elicitation techniques used, which are not always fully adapted to this kind of speakers.

Results And Discussions

This section details and discusses the behavior of the automatic anomaly detection approach for both read and spontaneous speech on the Typaloc corpus.

As reported in (Laaridh et al., 2015a) on read speech, the approach tends to detect more anomalies on patients with severe dysarthria. This behavior, though less distinct, seems preserved on spontaneous speech. Indeed, the correlation between the automatic anomaly rates and the DSD reaches 0.81 and 0.60 for read and spontaneous speech respectively. Similarly, figure 1 depicts the automatic anomaly rates for both read and spontaneous speech according to the patients' DSD rated on the read speech uniquely (this typical configuration permits to locate anomaly rates for both read and spontaneous speech for each speaker more easily). This confirms that the system is able to detect the evolution of dysarthria regardless of the production task performed by the patients (read or spontaneous speech). In a more detailed way, table 3 reports automatic anomaly detection rates on read and spontaneous speech grouped by population.

First, We observe that over the control speakers, the approach detects more anomalies over spontaneous speech compared to read speech. This could be related to the fact that our models of normal and abnormal phones (section 2.2.) were built over read speech only. Indeed, spontaneous speech may present more acoustic variability that is atypical (compared to read speech) without being pathological. This variation may be due to faster speech rate, false starts, hesitations and phone reductions more frequent in spontaneous speech. Figure 2 depicts the relation between the read speech DSD and the difference of automatic anomaly detection rates between spontaneous and read speech (anomaly rate over spontaneous speech - anomaly rate over read speech); each point representing one speaker (either control or patient). Observing table 3 and figure 2, we find that, as for control speakers, the automatic approach detects more anomalies on spontaneous speech for PD and CA patients.

Figure 2: Distribution of differences between spontaneous and read speech anomaly rates according to read speech DSD.

These observations are consistent with results found in [START_REF] Van Lancker Sidtis | Dramatic effects of speech task on motor and linguistic planning in severely dysfluent parkinsonian speech[END_REF][START_REF] Kempler | Effect of speech task on intelligibility in dysarthria: a case study of parkinson's disease[END_REF] on patients suffering from PD. In these papers, spontaneous speech was found to be less intelligible and contained more disfluencies than read speech. However, patients suffering from ALS present similar and even lower anomaly rates on spontaneous speech compared to reading (32% and 36% respectively). In our corpus, these patients have the most severe dysarthria (highest DSDs). Further analysis of this behavior is still necessary to study whether this observation is related to intrinsic characteristics of ALS which would affect more the reading task than spontaneous speech production. A second hypothesis is that this behavior is more linked to the severity of the dysarthria of the patients suffering from ALS than the pathology itself. In-deed, the spontaneous speech production task offers to patients more freedom to manage their fatigue, their speech rate and the different phones and sequences of phones or words to produce, which could lead to less anomalies at the phone level. Finally, table 3 shows a more important increase in automatic anomaly rates computed over spontaneous speech for control speakers compared to dysarthric patients. additionaly, Figure 2 suggests that the difference in anomaly rates between spontaneous and read speech is inversely proportional to the DSD measures. This would suppose that control speakers change more their productions according to the speech style (which results in our case in more anomalies detected over spontaneous speech considering the nature of our models built over read speech) while patients (especially severely dysarthric) loose this capacity to adapt themselves to different styles and tend to make their productions uniform regardless of the task (which results in less differences between measures computed over read and spontaneous speech). Table 4 reports the automatic anomaly detection rates per pathology, phonetic category and speech style.

For control speakers, all the phonetic categories show higher anomaly rates over spontaneous speech compared to read speech. Fricatives, however, present a more important increase in the anomaly rates from 7% over read speech to 23% over spontaneous speech. For PD patients, fricatives present the highest anomaly rates for both speech styles. Unlike other phonetic categories for which the increase of automatic anomaly rates over spontaneous speech is low, fricatives show an absolute increase of 29% (112% relative). This behavior suggests more an intensification of an already observed phenomenon (high anomaly rate over read speech) linked to the Parkinsonian dysarthria than the emergence of a new one. Observing patients suffering from ALS and even though anomaly rates are more stable across both speech styles, we find still that vowels present less anomalies on spontaneous than read speech (-4% and -18% for oral and nasal vowels respectively). The global decrease For each population, a one-way ANOVA was conducted in order to test the effect of speech style (2 levels: read speech, spontaneous speech). Figure 3 depicts the box plot representation of automatic anomaly rates for each population and speech style. For control and PD speakers, significant differences are found between read and spontaneous speech ((p<0.001,F(1,22)=28) and (p<0.05, F(1,14)=5.4) respectively). These differences are emphasized when focusing only on fricatives ( (p<0.001,F(1,22)=19) and (p<0.01,F(1,14)=14) for control and PD speakers respectively). The difference between both speech styles is less distinct for patients suffering for CA and ALS. Indeed, the speech style effect can be masked by the higher intra-pathological variability observed over both populations unlike patients suffering from PD, having all a mild dysarthria.

Conclusion

The study of the behavior of an automatic phone-based anomaly detection system over read and spontaneous dysarthric speech has shown different effects of the task and the speech style according to pathologies. ALS patients, in contrast to all other populations (control, PD, CA), showed more anomalies over read speech than spontaneous speech. Globally, the control speakers show the most important differences according to the speech style. Considering patients, the more severe the dysarthria is, the less difference between styles there is. A hypothesis could be that control speakers adapt their productions according to the speech style whereas dysarthric patients tend to gradually loose this capacity. By comparing the phonetic categories, fricatives show an important increase in terms of anomaly rates on spontaneous speech compared to read speech compared over the control speakers and the patients suffering from PD. Future work will examine the effect of the localization of the phones (first, second, etc. syllable) on the anomaly detection process in order to investigate further on these different behaviors.

Figure 1 :

 1 Figure1: Relationship between the automatic anomaly rate and the dysarthria severity degree when applied on the Typaloc corpus.

Figure 3 :

 3 Figure 3: Box plot of automatic anomaly rate per population and speech style.

Table 1 :

 1 Information related to the LSD-corpus used in the modeling process including the # of speakers, the # of recordings and the minimum and maximum Dysarthria Severity Degrees (DSD) per disease.

	Disease	# of	# of	(Min;Max)
		speakers recordings	DSD
	LSD	8	35	(1.5;3.0)
	Control	6	17	-

Table 2 :

 2 Information related to the Typaloc corpus including the # of speakers, the average Dysarthria Severity Degrees (DSD) and the average and (Min;Max) of recording duration (sec.) and # of phones from the automatic alignment per disease and speech style.

				Read speech			Spontaneous speech
	Disease	# of	Avg. DSD	Avg.	Avg. # of	Avg. DSD	Avg.	Avg. # of
		speakers		dur. (Min;Max) phones (Min;Max)		dur. (Min;Max) phones (Min;Max)
	ALS	12	2.0	111 (65;214)	532 (382;578)	2.0	102 (45;317)	463 (184;1089)
	PD	8	0.8	74 (48;122)	567 (550;599)	1.0	65 (40;109)	365 (315;404)
	CA	8	1.3	107 (73;142)	569 (544;595)	1.2	78 (40;124)	423 (223;838)
	Control	12	-	70 (56;82)	558 (552;568)	-	522 (296;1028)	4072 (2762;7104)

Table 3 :

 3 Average anomaly rate (%) per pathology and speech style computed over all phones.

	Disease Read speech Spontaneous speech
	ALS	35.8	31.9
	PD	10.6	17.8
	CA	20.6	24.4
	Control	5.4	13.7

Table 4 :

 4 Average automatic anomaly rate (%) computed per pathology, phonetic category and speech style (read and spontaneous speech).

								Read speech	Spontaneous speech
			Phonetic category	Control PD CA ALS	Control PD CA ALS
			Plosives			7	12	22	37	11	16	24	35
			Fricatives			7	26	48	37	23	55	53	38
			Nasal consonants	7	12	21	31	19	10	19	35
			Liquid consonants	7	9	23	41	15	9	27	44
			Oral vowels		2	6	10	33	11	10	13	29
			Nasal vowels		4	8	20	44	8	13	13	26
			Other			10	16	26	46	19	13	26	37
	of anomaly rates observed over ALS patients in table 3
	between read and spontaneous speech is the result of the
	decrease observed over vowels since consonants maintain
	similar anomaly rates between both speech styles.
		{ { { { *** * ns ns
		80
		60
	Automatic anomaly rate (%)	40
		20
		0
		Control_R Control_S	ALS_R	ALS_S	PD_R	PD_S	CA_R	CA_S

Acknowledgements

This work has been carried out thanks to the support of the BLRI Labex (ANR-11-LABEX-0036) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the "Investissements d'Avenir" French government program managed by the ANR, and thanks to the French ANR project Typaloc (ANR-12-BSH2-0003-03).