Olivier Pourret
email: olivier.pourret@univ-rennes1.fr.

Mélanie Davranche

Gérard Gruau

Aline Dia

0(176 &203/(;$7,21 :,7+ +80,& $&,'

Keywords: Rare earth elements, humic acid, binding, speciation, ultrafiltration, Model VI

$EVWUDFW The binding of rare earth elements (REE) to humic acid (HA) was studied by combining Ultrafiltration and Inductively Coupled Plasma Mass Spectrometry techniques. REE-HA complexation experiments were performed at various pH conditions (ranging from 2 to 10.5) using a standard batch equilibration method. Results show that the amount of REE bound to HA strongly increase with increasing pH. Moreover, a Middle REE (MREE) downward concavity is evidenced by REE distribution patterns at acidic pH. Modelling of the experimental data using Humic Ion Binding Model VI provided a set of log K MA values (i.e., the REE-HA complexation constants specific to Model VI) for the entire REE series. The log K MA pattern obtained displays a MREE downward concavity. Log K MA values range from 2.42 to 2.79. These binding constants are in good agreement with the few existing datasets quantifying the binding of REE with humic substances but quite different from a recently published study which evidence a lanthanide contraction effect (i.e., continuous increase of the constant from La to Lu). The MREE downward concavity displayed by REE-HA complexation pattern determined in this study compares well with results from REE-fulvic acid (FA) and REE-acetic acid complexation studies. This similarity in the REE complexation pattern suggests that carboxylic groups are the main binding sites of REE in HA. This conclusion is further illustrated by a detailed review of published studies for natural, organic-rich, river-and ground-waters which show no evidence of a lanthanide contraction effect in REE pattern. Finally, application of Model VI using the new, experimentally determined log K MA values to World Average River Water confirms earlier suggestions that REE occur predominantly as organic complexes ( LQ WKH S+ UDQJH between 5-5.5 and 7-8.5 (i.e., in circumneutral pH waters). The only significant difference as compared to earlier model predictions made using estimated log K MA values is that the experimentally determined log K MA values predict a significantly higher amount of Light REE bound to organic matter under alkaline pH conditions.

,QWURGXFWLRQ Rare Earth Elements (REE) are commonly used as tracers for geochemical processes in natural waters [START_REF] Elderfield | The rare earth elements in seawater[END_REF][START_REF] Henderson | Rare earth element geochemistry[END_REF][START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF][START_REF] Smedley | The geochemistry of rare earth elements in groundwater from the Carnmenellis area, southwest England[END_REF][START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF][START_REF] Johannesson | Rare earth elements as geochemical tracers of regional groundwater mixing[END_REF][START_REF] Johannesson | Origin of rare earth element signatures in groundwaters of circumneutral pH from southern Nevada and eastern California, USA[END_REF].

Ground-and river-waters do not exhibit uniform REE patterns [START_REF] Goldstein | Rare earth elements in river waters[END_REF][START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF][START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF]. Systematic variations in REE patterns occur which have been attributed, in part, to the presence of two REE pools -so-called "dissolved" and "colloidal" pools -having different REE fractionation pattern. For example, it is often suggested that REE patterns of rivers result from the mixing between (i) a low REE concentration, dissolved pool, Light-REE (LREE) depleted, but Heavy-REE (HREE) enriched related to Upper Continental Crust (UCC) [START_REF] Taylor | The Continental Crust: Its composition and evolution[END_REF] and (ii) a REE-rich colloidal phase having a Middle-REE (MREE) downward concavity pattern, namely a pattern that exhibits both (La/Sm) UCC ratio below 1 and (Gd/Yb) UCC ratio above 1 [START_REF] Goldstein | Rare earth elements in river waters[END_REF][START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF][START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF]. Humic substances constitute a significant part of the colloidal phase in both ground-and river-waters [START_REF] Thurman | Organic Geochemistry of Natural Waters[END_REF].

Consequently, interpreting and understanding the significance of REE fractionation pattern variability in these waters rely, for a large part, on a better description of the binding of REE to humic materials (HM) and of the REE fractionation patterns that may occur during this binding.

Models based on thermodynamics principles offer an interesting perspective in order to predict the complexation of REE with HM and thus to explore the extent to which the shape of ground-and river-water REE patterns depends on REE fractionation by HM.

Modelling calculations were already performed in seawater [START_REF] Cantrell | Rare earth element complexation by carbonate and oxalate ions[END_REF][START_REF] Byrne | Rare earth element scavenging in seawater[END_REF][START_REF] De Baar | Solution chemistry of the rare earth elements in seawater[END_REF] and groundwaters [START_REF] Wood | The aqueous geochemistry of the rare-earth elements and yttrium. 1. Review of the available low-temperature data for inorganic complexes and inorganic REE speciation in natural waters[END_REF][START_REF] Lee | Examination of comparative rare earth element complexation behavior using linear free-energy relationships[END_REF]Johannesson et al., 1996b). They evidence that hydroxides and carbonates [START_REF] Cantrell | Rare earth element complexation by carbonate and oxalate ions[END_REF][START_REF] Lee | Complexation of trivalent rare earth elements (Ce, Eu, Gd, Tb, Yb) by carbonate ions[END_REF][START_REF] Liu | Comprehensive investigation of yttrium and rare earth element complexation by carbonate using ICP-Mass Spectrometry[END_REF][START_REF] Luo | Carbonate complexation of Yttrium and the rare earth elements in natural rivers[END_REF] are the major inorganic complexing ligands of REE. However, organic complexation is often not taken into account in these calculations. Studies on REE -dissolved organic material (DOM) interactions and in particular humic and fulvic acids (HA and FA) are sparse in all respect [START_REF] Dupré | Major and trace elements associated with colloids in organic-rich river waters: ultrafiltration of natural and spiked solutions[END_REF][START_REF] Tang | Speciation of rare earth elements in natural terrestrial waters: Assessing the role of dissolved organic matter from the modeling approach[END_REF][START_REF] Johannesson | Rare earth element concentrations and speciation in organic-rich blackwaters of the Great Dismal Swamp, Virginia, USA[END_REF]. Natural organic molecules contain numerous and chemically different binding sites, and thus it is difficult to define discrete equilibrium constants for each complexation reaction [START_REF] Crawford | PHREEQEV: the incorporation of a version of Model V for organic complexation in aqueous solutions into the speciation code PHREEQE[END_REF]. Thus, addressing complexation with DOM in existing aqueous speciation models is not obvious.

Until recently, REE-FA or -HA complexation constants were generally determined for a single REE (e.g., for HA: Sm-HA, Eu-HA, Tb-HA and Dy-HA) [START_REF] Maes | A modified Schubert method for the measurement of the stability of europium humic acid complexes in alkaline conditions[END_REF][START_REF] Bidoglio | Complexation of Eu and Tb with fulvic acids as studied by time-resolved laser induced fluorescence[END_REF][START_REF] Moulin | Complexation behaviour of humic substances towards actinides and lanthanides studied by Time-Resolved Laser-Induced Spectrofluometry[END_REF][START_REF] Dierckx | Mixed complex formation of Eu 3+ with humic acid and a competing ligand[END_REF][START_REF] Fairhurst | The influence of humic acid on the adsorption of europium onto inorganic colloids as a function of pH[END_REF][START_REF] Franz | Complexation of Samarium(III) and Americium(III) with humic acid at very low metal concentrations[END_REF][START_REF] Lead | Europium binding by fulvic acids[END_REF][START_REF] Lippold | Effect of humic acid on the pH-dependent adsorption of terbium(III) onto geological materials[END_REF] or a set of REE (Ce, Eu, Gd, Tm and Lu) [START_REF] Takahashi | Simultaneous determination of stability constants of humate complexes with various metal ions using multitracer technique[END_REF] while accurate stability constants are needed for the 14 naturally occurring REE to model speciation of natural water. Only two studies reporting REE-humate binding constants for the 14 naturally occurring REE have been so far published. They have unfortunately yielded to somewhat different results. [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF][START_REF] Yamamoto | Interpretation of REE patterns in natural water based on the stability constants[END_REF] studied complexation of REE with Suwannee River FA (SRFA) and Suwannee River HA (SRHA) using a solvent extraction method. Patterns of REE-FA and REE-HA complexation constants obtained by these authors show a general MREE downward concavity similar to that obtained for REEdiacetic acid complexation, sometimes superimposed to an overall increase of complexation constants from LREE to HREE. By contrast, [START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF] studied complexation of REE with SRFA, Leonardite coal HA (LHA) and Elliot soil HA (EHA) using an EDTAligand competition method. Complexation patterns obtained by these authors are quite different from those reported by [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF] exhibiting a lanthanide contraction effect, i.e., a regular and progressive increase of the constants from La to Lu. It must be added that experimental pH range are different in both of these studies ranging between 4 and 5.5 in Yamamoto et al. studies (2005;[START_REF] Sonke | Lanthanide-humic substances complexation. II. Calibration of Humic Ion-Binding Model V[END_REF] and 6 and 9 in Sonke and Salters' one (2006).

In this contribution, in order to further test evidences provided by both studies [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF][START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF] and provide a more accurate REE-HA complexation dataset, experiments of REE-Aldrich HA (AHA) complexation were performed for the 14 naturally occurring REE simultaneously. This new dataset was obtained using a third experimental method which combines an ultrafiltration technique and the Inductively Coupled Plasma Mass Spectrometry method. Humic Ion Binding Model VI (hereafter denoted as Model VI) was used to model the experimental results [START_REF] Tipping | Humic Ion-Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF]. Therefore, the constants reported are not intrinsic equilibrium constants but Model VI specific equilibrium constants (i.e., log K MA values). Considering the key role played by organic colloids in natural terrestrial waters, the new results were also used to revisit the significance of REE patterns in organic-rich, ground-and river-waters. Finally, the new constants were input in Model VI to reinvestigate World Average River Water [START_REF] Tang | Speciation of rare earth elements in natural terrestrial waters: Assessing the role of dissolved organic matter from the modeling approach[END_REF] and further constrain the speciation of REE in river waters.

0DWHULDOV DQG 0HWKRG ([SHULPHQWDO %LQGLQJ RI 5DUH (DUWK (OHPHQWV E\ +XPLF $FLG

All chemicals used in this study were of analytical grade, and all the experimental solutions used prepared with doubly deionized water (MilliQ system, Millipore™). REE-HA complexes were prepared in polyethylene containers previously soaked in 10 % Ultrapure HNO 3 for 48 h at 60 °C, then rinsed with MilliQ water for 24 h at 60 °C to remove all possible REE contamination sources. Synthetic REE solutions were prepared from nitrate REE standards (10 ppm, Accu Trace™ Reference Standard). All experiments were carried out at room temperature, i.e., 20 °C ± 2.

+XPLF DFLG

Purified humate, referred to below as HA (humic acid), was obtained from synthetic Aldrich™ humic acid (Aldrich™, H1, 675-2) following the protocol described by Vermeer et al. [START_REF] Vermeer | Adsorption of humic acid to mineral particles. 1. Specific and Electrostatic Interactions[END_REF]. HA sample was freeze-dried and stored in a glass container at room temperature. HA obtained was ash free and in its protonated form, with the following elemental composition (as mass fraction): C = 55.8 %, O = 38.9 %, H = 4.6 %, N = 0.6 %. REE concentrations in HA were below the detection limit of ICP-MS measurement (i.e., <1 ppt). HA has a mean molecular weight of 23 kDa [START_REF] Vermeer | Adsorption of humic acid to mineral particles. 1. Specific and Electrostatic Interactions[END_REF]. Prior to use, the freeze-dried humate was resuspended overnight in an 0.001 mol L -1 NaCl electrolyte solution at pH = 10, to ensure complete dissolution of the sample [START_REF] Davranche | Impact of humate complexation on the adsorption of REE onto Fe oxyhydroxide[END_REF][START_REF] Davranche | Adsorption of REE(III)-humate complexes onto MnO 2 : Experimental evidence for cerium anomaly and lanthanide tetrad effect suppression[END_REF]. Although AHA is sometimes criticized as a weak model for humic substances (HS), its ion-binding behavior and physico-chemical properties are rather similar to those of other HS [START_REF] Avena | Volume and structure of humic acids studied by viscometry pH and electrolyte concentration effects[END_REF][START_REF] Milne | Generic NICA-Donnan Model parameters for proton binding by humic substances[END_REF]2003;[START_REF] Saito | Electrostatic interactions models for ion binding to humic substances[END_REF].

([SHULPHQWDO VHWXS GHVFULSWLRQ

REE complexation with HA was investigated using a standard batch equilibration technique. REE (50 ppb of each REE) and HA (5, 10 and 20 mg L -1 ) were placed together in solution, at an ionic strength of 0.001 mol L -1 and at pH values ranging from 2.18 to 10.44. Prior to addition of HA, pH-solution was about 4. The initial hydroxide and carbonate concentrations were thus negligible and concentrations of LnOH 2+ and LnCO 3 2-were therefore also negligible. The pH was measured with a combined Radiometer Red Rod electrode.

The electrode was calibrated with WTW standard solutions (pH 4, 7 and 10). The accuracy of the pH measurement was ±0.05 pH unit. Experimental solutions were stirred for 48 h (equilibrium time determined from preliminary kinetic experiments) to allow equilibration and partitioning of REE between the aqueous solution and the humate suspension. Solution aliquots of about 10 mL were sampled twice: first at the beginning of the experiment; then after 48 h at equilibrium state. REE complexed by the HA were separated from the remaining inorganic REE by ultrafiltration. Ultrafiltrations were carried out by centrifugating the 10 mL solution samples through 15 mL centrifugal tubes equipped with permeable membranes of 5 kDa pore size (Millipore Amicon Ultra-15). All centrifugal filter devices used were washed and rinsed with 0.1 mol L -1 HCl and MilliQ water two times before use in order to minimize contamination. Centrifugations were performed using a Jouan G4.12 centrifuge with swinging bucket rotor at 3000 g for 30 minutes. The centrifugal force allowed the REE-HA complexes to be quantitatively separated from inorganic REE species. The used membrane can retain the humic material, while permitting passage of smaller solutes. REE-HA complexes have a mean molecular weight of 23 kDa, and are therefore caught up by the 5 kDa membrane whereas inorganic REE species went through it. The filtrate contains only the free REE, while the retentate contains HA and associate bound REE. The amount of metal bound is determined by difference. The selectivity of the 5 kDa membrane regards to the REE-HA complexes was verified by monitoring the Dissolved Organic Carbon (DOC) contents of the ultrafiltrates. Results show that the latter were systematically lower or equal to blank values ( 0.1 ppm). Even if the passage of small amounts of low molecular weight FA through the ultrafiltration membrane was previously described [START_REF] Nordén | The binding of strontium and europium by an aquatic fulvic acid-ion exchange distribution and ultrafiltration studies[END_REF] in such measurement of metal binding by HS, we considered that the purified HA (23 kDa) could not pass through the membrane (5 kDa) as illustrated by ultrafiltrate DOC values lower or equal to blank ones. Possible adsorption of inorganic REE species onto the membrane or onto cell walls was also monitored. Inorganic REE solutions of known REE concentration were ultrafiltered several times. Results showed that between 98.91 (for Ho) and 99.98 % (for Yb)

of the REE present in solution were recovered in the ultrafiltrates, demonstrating that no REE was adsorbed on the membranes nor on the walls of the cell devices used.

In the following, REE complexation with HA is described by quantifying the amount of REE that remains into the <5 kDa ultrafiltrates after equilibration of the REE-bearing aqueous solutions with HA and by comparing this amount to the amount of REE which has been introduced initially into the experimental solutions. All the removed REE are assumed to have been complexed by the 23 kDa HA. REE concentrations were determined at Rennes 1 University using an Agilent Technologies TM HP4500 ICP-MS instrument. Quantitative analyses were performed using a conventional external calibration procedure. Three external standard solutions with REE concentrations similar to the analyzed samples were prepared from a multi-REE standard solution (Accu Trace TM Reference, 10 mg L -1 , USA). Indium was added to all samples as an internal standard at a concentration of 0.87 µmol L -1 (100 ppb) to correct for instrumental drift and possible matrix effects. Indium was also added to the external standard solutions. Calibration curves were calculated from measured REE/indium intensity ratios. The accuracy on REE analysis in our laboratory as established from repeated analyses of multi-REE standard solution (Accu Trace TM Reference, USA) and of the SLRS-4 water standard is < ± 2.5 % [START_REF] Dia | The distribution of rare earth elements in groundwaters: assessing the role of source-rock composition, redox changes and colloidal particle[END_REF][START_REF] Davranche | Adsorption of REE(III)-humate complexes onto MnO 2 : Experimental evidence for cerium anomaly and lanthanide tetrad effect suppression[END_REF]. Chemical blanks of individual REE were all lower than detection limit (1 ppt), which is negligible since they are three to four orders of magnitude lower than the concentrations measured in the synthetic solutions used in the complexation experiments. DOC concentrations were determined at Rennes 1 University using a Shimadzu 5000 TOC analyzer. The accuracy of DOC concentration measurements is estimated at ± 5 % as determined by repeated analyses of freshly prepared standard solutions (potassium biphtalate). In order to check that no retention of REE occurs inside the membrane during ultrafiltration, mass balance calculations were performed. The initial concentration of each element is compared with the sum of each element concentration in the ultrafiltrate and in the retentate. In the presented experiments, mass balanced calculations show that > 98 % of the REE and DOC were recovered.

Moreover, in order to verify that no precipitation occurs, samples were filtered at 0.2 µm before ultrafiltration. Concentrations of REE and HA were systematically identical within analytical uncertainties in the 0.2 µm filtrates and the raw samples.

+XPLF ,RQ %LQGLQJ 0RGHO 9,

Humic Ion Binding Model VI (Model VI) has been described in detail by Tipping [START_REF] Tipping | Humic Ion-Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF]. The model is a discrete binding site model which takes into account For more details on whether mono-, bi and tridentate sites may form see [START_REF] Tipping | Humic Ion-Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF]. By considering results from many datasets, a XQLYHUVDO DYHUDJH YDOXH RI /. 1 is obtained, and a correlation established between log K MB and log K MA [START_REF] Tipping | Humic Ion-Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF]. Then, a single adjustable parameter (log K MA ) is necessary to fully describe metal complexation with HA in Model VI. Model VI parameters for HA are presented in Table 1. Model VI was preferentially selected because this model is a widely accepted metal-organic matter speciation code whose capability to model Eu complexation with HM has been already tested and proved to perform reasonably well [START_REF] Lead | Europium binding by fulvic acids[END_REF].

Binding of the first hydrolysis species REE(OH) to humics was not included in Model VI simulations.

Location of Table 1.

5HVXOWV ([SHULPHQWDO UHVXOWV

Experimental data are reported in Appendix A and illustrated for three REE (La, Eu, Lu) in Fig. 1. REE complexation by HA is examined by considering the proportion of REE-HA complexes formed as a function of pH. As shown in Fig. 1, the proportion of REE-HA complexes increases with increasing pH. It also depends on the HA concentration of the experimental solution. More specifically, the pH at which 100 % of the REE are complexed by HA decreases with increasing HA concentration: pH § 4, 5.5 and 7 for HA concentrations of 20, 10 and 5 mg L -1 , respectively (see Fig. 1). These results are in good agreement with previously published data [START_REF] Maes | A modified Schubert method for the measurement of the stability of europium humic acid complexes in alkaline conditions[END_REF][START_REF] Dierckx | Mixed complex formation of Eu 3+ with humic acid and a competing ligand[END_REF][START_REF] Franz | Complexation of Samarium(III) and Americium(III) with humic acid at very low metal concentrations[END_REF][START_REF] Lippold | Effect of humic acid on the pH-dependent adsorption of terbium(III) onto geological materials[END_REF]. The observed pH-dependence of REE complexation by HA can be explained by the de-protonation of the HA carboxylic and phenolic surface groups at increasing pH, which is a classical feature of metal ion complexation by humic acid [START_REF] Fairhurst | The influence of humic acid on the adsorption of europium onto inorganic colloids as a function of pH[END_REF][START_REF] Lippold | Effect of humic acid on the pH-dependent adsorption of terbium(III) onto geological materials[END_REF]. Interestingly, patterns of the proportions of REE-HA exhibits a MREE downward concavity (i.e., (La-HA)/(Sm-HA) < 1 and (Gd-HA)/(Yb-HA) > 1) when the amount of REE complexed with HA is sufficiently low to evidence possible fractionation among the REE series (i.e., under acidic pH conditions; Fig. 2 andAppendix A). The same feature was already observed in complexation experiments of the REE with AHA using the dialysis method [START_REF] Davranche | Adsorption of REE(III)-humate complexes onto MnO 2 : Experimental evidence for cerium anomaly and lanthanide tetrad effect suppression[END_REF]. proportion with increasing pH. Log K MA values increase with HA concentration of experimental solutions. The overall difference is of ca. 0.3 for all REE, which corresponds to a difference of ca. 12 % on estimated log K MA values. As can be seen in Fig. 3, a 12 % difference on log K MA values may result in a difference of up to 20 % on the calculated proportion of REE-HA complex if the pH and the HA concentration are low. The default value is computed by taking the mean between the three sets of values. This procedures means that equal weight is given to all the data sets obtained for different HA concentration levels. It implies that differences in log K MA for a given type of humic material are due to variations from one sample to another [START_REF] Tipping | Humic Ion-Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF]. The adopted values are listed in Location of Fig. 3 and Table 2.

Location of

Discussion

Comparison with published results

Data from previous studies [START_REF] Moulin | Complexation behaviour of humic substances towards actinides and lanthanides studied by Time-Resolved Laser-Induced Spectrofluometry[END_REF][START_REF] Fairhurst | The influence of humic acid on the adsorption of europium onto inorganic colloids as a function of pH[END_REF][START_REF] Lippold | Effect of humic acid on the pH-dependent adsorption of terbium(III) onto geological materials[END_REF][START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF] were modelled using Model VI to compare them with the newly obtained set of log K MA . Previous studies [START_REF] Moulin | Complexation behaviour of humic substances towards actinides and lanthanides studied by Time-Resolved Laser-Induced Spectrofluometry[END_REF][START_REF] Fairhurst | The influence of humic acid on the adsorption of europium onto inorganic colloids as a function of pH[END_REF][START_REF] Lippold | Effect of humic acid on the pH-dependent adsorption of terbium(III) onto geological materials[END_REF] provide data for only a limited number of REE, namely Tb, Dy and Eu.

Experimental conditions and fit results for these three studies are presented in K MA (La-HA)/log K MA (Sm-HA) < 1 and log K MA (Gd-HA)/log K MA (Yb-HA) < 1). This continuous increase of log K MA values from La to Lu as obtained for the LHA has been ascribed to a lanthanide contraction effect, and is quantified as the numerical difference between the Lu and La binding constantV L Lu-La in [START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF]. Such a feature (i.e., lanthanide contraction) have already been attributed to REE binding to coal derived HA [START_REF] Takahashi | Simultaneous determination of stability constants of humate complexes with various metal ions using multitracer technique[END_REF].

Location of Table 3 andTable 4.

The major difference between the results obtained here and the only dataset so far published for the 14 naturally occurring REE with HA is thus in the shape of the log K MA values: MREE downward concavity here against linear increase from La to Lu for data published by [START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF]. They compared the constants they found for REE-LHA complexation with those found for 101 organic ligands [START_REF] Byrne | Comparative complexation behavior of the rare earths[END_REF]. They further suggested that the lanthanide contraction effect they observed for LHA was in fact shared by a large number of organic ligands and could thus be a general feature of REE complexation by organic compounds. In addition to LHA, [START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF] also determined REE complexation constants for two other HM sources -namely, fulvic acid from the Suwannee River and Elliot soil humic acid -using the same ligand-ligand technique as that used for LHA. They also evidenced a lanthanide contraction effect of their constant pattern. In the present study, no observation was found that a lanthanide contraction effect should exist in the complexation constants of REE by organic matter. Instead, the existing databank [START_REF] Wood | The aqueous geochemistry of the rare-earth elements: Critical stability constants for complexes with simple carboxylic acids at 25°C and 1 bar and their application to nuclear waste management[END_REF][START_REF] Byrne | Comparative complexation behavior of the rare earths[END_REF] points out that a MREE downward concavity of some constant pattern could occurred, as obtained in the present study.

Location of Fig. 4.

Complexation constants of REE with SRFA and SRHA were also determined by [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF][START_REF] Yamamoto | Interpretation of REE patterns in natural water based on the stability constants[END_REF] using a solvent extraction technique (Fig. 4a; see also Fig. 1 in [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF]. Pattern of log (REE-FA) and log (REE-HA) looks very much like that found in the present study for REE-HA, being characterized by a MREE downward concavity shape (i.e., log K MA (La-HA)/log K MA (Sm-HA) < 1 and log K MA (Gd-HA)/log K MA (Yb-HA) > 1). Binding of the REE to humic substances occurs mainly through carboxylic functions [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF]. In this respect, simple organic compounds possessing carboxylic functional groups such as acetate can be regarded as analogue of complex organic matter, such as HA and FA [START_REF] Wood | The aqueous geochemistry of the rare-earth elements: Critical stability constants for complexes with simple carboxylic acids at 25°C and 1 bar and their application to nuclear waste management[END_REF]. Thus, log K MA pattern of REE complexation with FA, HA and acetic acid can be together compared (Fig. 4) [START_REF] Wood | The aqueous geochemistry of the rare-earth elements: Critical stability constants for complexes with simple carboxylic acids at 25°C and 1 bar and their application to nuclear waste management[END_REF][START_REF] Byrne | Comparative complexation behavior of the rare earths[END_REF]. As can be seen, patterns of log K MA values for acetic and diacetic acids do not evidence a lanthanide contraction effect: they are MREE downward concave, The argument used by [START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF] that a lanthanide contraction effect is observed in the constant pattern of many organic compounds is questionable. Indeed, their demonstration was built on the fact that a positive correlation exists when L Lu-La of organic FRPSRXQGV LV SORWWHG DJDLQVW WKH DYHUDJH 5(( FRQVWDQW ave ). REE stability constant is not always a simple function of atomic number. As suggested by [START_REF] Byrne | Comparative complexation behavior of the rare earths[END_REF], REE are best viewed as two series of elements corresponding to the LREE and HREE groups. The REE patterns of complexation constants with many organic ligands [START_REF] Wood | The aqueous geochemistry of the rare-earth elements: Critical stability constants for complexes with simple carboxylic acids at 25°C and 1 bar and their application to nuclear waste management[END_REF][START_REF] Byrne | Comparative complexation behavior of the rare earths[END_REF] or some inorganic ligands like NO 3 - [START_REF] Wood | The aqueous geochemistry of the rare-earth elements and yttrium. 1. Review of the available low-temperature data for inorganic complexes and inorganic REE speciation in natural waters[END_REF][START_REF] Millero | Stability constants for the formation of rare earth inorganic complexes as a function of ionic strength[END_REF] are characterized by a downward concavity centred on MREE. This shape corresponds to a (La/Sm) ratio below 1 and (Gd/Yb) ratio above 1 (Fig. 4a). Thus, such shapes are best viewed when REE are considered as two subgroups. Indeed, (La/Lu) ratio does not give an accurate description of the constants pattern. Let us for example consider a (La/Lu) ratio < 1. Such a value may iminoacetic acids such as EDTA (Fig. 5). Observed differences between experiments presented in this study, and literature data [START_REF] Takahashi | Simultaneous determination of stability constants of humate complexes with various metal ions using multitracer technique[END_REF][START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF][START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF][START_REF] Yamamoto | Interpretation of REE patterns in natural water based on the stability constants[END_REF] strongly suggests the heterogeneity of the complexing site (i.e., the coexistence of weak and strong binding sites) in the humic acid [START_REF] Kubota | Complex formation of Eu(III) with humic acid and polyacrylic acid[END_REF]. The main features of our observations is that one can interprets differences as due to high concentration of weak sites (acetic-like sites) and low concentrations of strong sites (EDTA-like sites). The weak sites determine the behavior of humic complexation at high metal concentrations, whereas the strong sites, although their concentration is only in the range of a few percent of the weak sites, determine the complexation strength of humic substances at trace metal concentrations [START_REF] Hummel | Binding of radionuclides by humic substances: the "conservative roof" approach[END_REF][START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF]. Our study HS/Ln ratios are between 5 and 20, whereas in [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF] experiments they are close to 80, in Sonke and Salters (2005) between 550 and 600 (in LHA experiments) and in [START_REF] Takahashi | Simultaneous determination of stability constants of humate complexes with various metal ions using multitracer technique[END_REF] as high as 100,000. Such a feature was already observed by [START_REF] Bidoglio | Complexation of Eu and Tb with fulvic acids as studied by time-resolved laser induced fluorescence[END_REF] and [START_REF] Moulin | Complexation behaviour of humic substances towards actinides and lanthanides studied by Time-Resolved Laser-Induced Spectrofluometry[END_REF] who evidenced that the REE concentration exerts a relatively great effect on the HM complexing capacity. This may be explained by a loading effect: at low loading, stronger sites will be favored whereas at large loadings, weaker sites will be involved in the complexation.

This "HS/Ln ratio" effect is further marked by pH conditions: phenolic sites are predominant at alkaline pH whereas carboxylic are dominant binding sites at acidic pH. Indeed, our study pH spans a 2 to 10 range with an analytical window lied in the 2-6 range, whereas in [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF][START_REF] Yamamoto | Interpretation of REE patterns in natural water based on the stability constants[END_REF] experiments pH varies between 4 and 5.5 and, in Sonke and Salters (2005) it varies between 6 and 9. Eventually, the understanding of this fractionation is not obvious and such a MREE downward concavity remains to be explained.

However, the similar MREE downward concavity patterns observed for REE complexation with HA (present study), with FA [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF] and with model molecule of carboxylic sites (acetic acid) [START_REF] Wood | The aqueous geochemistry of the rare-earth elements: Critical stability constants for complexes with simple carboxylic acids at 25°C and 1 bar and their application to nuclear waste management[END_REF], evidence that this type of fractionation could be expected in natural systems. Endly, the comparison of [START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF], [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF], and present datasets allow validating the new log K MA (REE-HA) dataset presented in this study which could be then used to predict REE speciation in natural waters.

REE speciation in natural river and soil waters

Location of Fig. 6.

Several authors confer a major role to the colloidal fraction in the transport of REE by rivers [START_REF] Hoyle | The behaviour of rare earth elements during mixing of river and sea waters[END_REF][START_REF] Goldstein | Rare earth elements in river waters[END_REF][START_REF] Cantrell | Rare earth element complexation by carbonate and oxalate ions[END_REF][START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF][START_REF] Sholkovitz | Chemical evolution of rare earth elements: fractionation between colloidal and solution phases of filtered river water[END_REF]. [START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF] proposed for example the existence of two REE compartments in river waters (Fig. 6): (i) a "truly" dissolved pool, LREE depleted, but HREE enriched (Fig. 6a) and (ii) a "colloidal" pool LREE enriched and HREE flat, i.e., close to UCC (Fig. 6b) [START_REF] Taylor | The Continental Crust: Its composition and evolution[END_REF]. Several studies have suggested that the HREE enrichment of the dissolved pool may be due to the greater solubility of HREE carbonate complexes as compared to LREE carbonate ones [START_REF] Goldstein | Rare earth elements in river waters[END_REF][START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF][START_REF] Sholkovitz | Chemical evolution of rare earth elements: fractionation between colloidal and solution phases of filtered river water[END_REF]. However, one of the characteristics of some river waters is that (La/Sm) UCC and (Gd/Yb) UCC ratios are commonly lower than and higher than 1, respectively, producing a concave REE pattern (Fig. 6b) [START_REF] Goldstein | Rare earth elements in river waters[END_REF][START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF][START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF][START_REF] Viers | Chemical weathering in the drainage basin of a tropical watershed (Nsimi-Zoetele site, Cameroon): comparison between organic-poor and organic-rich waters[END_REF][START_REF] Deberdt | New insights about the rare earth elements (REE) mobility in river waters[END_REF]. Such a characteristic is also well evidenced in organic rich groundwaters [START_REF] Viers | Chemical weathering in the drainage basin of a tropical watershed (Nsimi-Zoetele site, Cameroon): comparison between organic-poor and organic-rich waters[END_REF][START_REF] Dia | The distribution of rare earth elements in groundwaters: assessing the role of source-rock composition, redox changes and colloidal particle[END_REF][START_REF] Deberdt | New insights about the rare earth elements (REE) mobility in river waters[END_REF][START_REF] Gruau | Controls on the distribution of rare earth elements in shallow groundwaters[END_REF]. As shown on Fig. 7, (Gd/Yb) UCC ratios increase while DOC content increases [START_REF] Goldstein | Rare earth elements in river waters[END_REF][START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF][START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF][START_REF] Viers | Chemical weathering in the drainage basin of a tropical watershed (Nsimi-Zoetele site, Cameroon): comparison between organic-poor and organic-rich waters[END_REF][START_REF] Deberdt | New insights about the rare earth elements (REE) mobility in river waters[END_REF]. These features are further illustrated by ultrafiltration studies of organic rich river-and ground-water samples. They indicate the absence of fractionation of HREE relative to LREE with decreasing pore-size cutoff (allowing colloidal separation) in the wide range of investigated pH (from 5.6 to 7.7) [START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF][START_REF] Viers | Chemical weathering in the drainage basin of a tropical watershed (Nsimi-Zoetele site, Cameroon): comparison between organic-poor and organic-rich waters[END_REF][START_REF] Douglas | Geochemistry of suspended particulate matter (SPM) in the Murray-Darling River system: a conceptual isotopic/geochemical model for the fractionation of major, trace and rare earth elements[END_REF][START_REF] Dupré | Major and trace elements associated with colloids in organic-rich river waters: ultrafiltration of natural and spiked solutions[END_REF][START_REF] Dia | The distribution of rare earth elements in groundwaters: assessing the role of source-rock composition, redox changes and colloidal particle[END_REF][START_REF] Deberdt | New insights about the rare earth elements (REE) mobility in river waters[END_REF], they even evidence for some of them a MREE downward concavity fractionation (e.g., Fig. 15 in [START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF]. However, such a fractionation alone could not explain the whole REE pattern fractionation. Indeed, Johannesson et al. (1996a) suggested that MREE enrichment in hypersaline waters is the result of mineral weathering due to dissolution of MREE-enriched Fe-and Mn-rich particulates or surface coatings. [START_REF] Hannigan | The development of middle rare earth element enrichments in freshwaters: weathering of phosphate minerals[END_REF] also proposed that MREE enrichment has to be related to the weathering of co-precipitated phosphates minerals. The present study demonstrates that organic substances plays a major control onto REE speciation and thus onto REE fractionation pattern. Based on this ability of HA to preferentially complex MREE, REE fractionation patterns at the Earth' s surface can be revisited. DOC-rich river-and groundwaters REE patterns may reflect a mixing between (i) dissolved fraction of DOC-rich soil solutions, surface runoff and river waters which display MREE downward concavity patterns [START_REF] Viers | Chemical weathering in the drainage basin of a tropical watershed (Nsimi-Zoetele site, Cameroon): comparison between organic-poor and organic-rich waters[END_REF][START_REF] Dia | The distribution of rare earth elements in groundwaters: assessing the role of source-rock composition, redox changes and colloidal particle[END_REF][START_REF] Deberdt | New insights about the rare earth elements (REE) mobility in river waters[END_REF][START_REF] Gruau | Controls on the distribution of rare earth elements in shallow groundwaters[END_REF]; and (ii) river suspended particles which are slightly LREE enriched inherited from preferential adsorption of LREE [START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF].

Location of Fig. 7. 2007). Model VI predicts that (i) REE occur predominantly as organic complexes (REE-HM 0 %) in the pH range between 5-5.5 and 7-8.5 (i.e., circumneutral pH waters), (ii) REE-HM complexes become progressively less important with increasing atomic number across the REE series, and (iii) pH at which proportion of REE-HM species is maximum decreases from La to Lu. The only noticeable difference is that the new fitted constants allow Model VI to calculate significantly higher proportions of REE complexed with organic matter at high pH conditions than with extrapolated constants as already proposed by [START_REF] Sonke | Lanthanide-humic substances complexation. II. Calibration of Humic Ion-Binding Model V[END_REF] using Model V. As an example, with the fitted constants Model VI predicts La-HM ~ 85 % at pH 9, while with extrapolated constants it only predicts 60 % of La-HM. However, these new calculations are also a validation of the LFER method [START_REF] Tang | Speciation of rare earth elements in natural terrestrial waters: Assessing the role of dissolved organic matter from the modeling approach[END_REF][START_REF] Pourret | Organic complexation of rare earth elements in natural waters: Evaluating model calculations from ultrafiltration data[END_REF]. Moreover, these results are coherent with simple speciation calculations performed by Takahashi and co-workers [START_REF] Takahashi | Simultaneous determination of stability constants of humate complexes with various metal ions using multitracer technique[END_REF][START_REF] Takahashi | Comparison of adsorption behavior of multiple inorganic ions on kaolinite and silica in the presence of humic acid using the multitracer technique[END_REF] which led them to speculate that humate complexes should be the dominant species of REE(III) dissolved in natural aquifers where DOM occurs significantly.

Conclusions

Experimental studies of REE-HA binding were carried out using an ultrafiltration method. A strong pH dependence resulting in a significant increase in the proportion of REE-HA with increasing pH and a MREE downward concavity fractionation at acidic pH were observed. Moreover, modelling was performed using Humic Ion Binding Model VI.

Experimental results were fitted to obtain a set of constants (log K MA ). The constants presented in our study are in good agreement with some literature constants. However, they are different from those published by [START_REF] Takahashi | Simultaneous determination of stability constants of humate complexes with various metal ions using multitracer technique[END_REF] and [START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF] which evidences a lanthanide contraction effect. Model VI constants in this study indicate that REE-HA complexation involved a fractionation similar to the REE complexation with simple carboxylic groups such as acetates. The similar MREE downward concavity pattern observed for REE complexation with HA (this study), with FA [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF] and with simple organic acid such as acetic acid [START_REF] Wood | The aqueous geochemistry of the rare-earth elements: Critical stability constants for complexes with simple carboxylic acids at 25°C and 1 bar and their application to nuclear waste management[END_REF][START_REF] Byrne | Comparative complexation behavior of the rare earths[END_REF], illustrates that this type of fractionation could be expected in natural systems. All these observations allow validating the present log K MA (REE-HA) dataset that can be, therefore, used with confidence to predict REE speciation in natural waters. Essentially, experiments from this study coupled to observation made by [START_REF] Takahashi | Simultaneous determination of stability constants of humate complexes with various metal ions using multitracer technique[END_REF], [START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF][START_REF] Sonke | Lanthanide-humic substances complexation. II. Calibration of Humic Ion-Binding Model V[END_REF] and [START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF] allow covering a wide range of experimental conditions both in HS/Ln ratio and in pH range (from 2 to 10) and are thus complementary. Moreover, the results of the present study shed more light on the REE cycle at the Earth's surface. They confirm that organic REE speciation plays a major role onto REE fractionation in natural organic-rich river-and groundwaters. Dissolved fractions of DOC-rich soil solutions, surface runoff, and river waters display MREE downward concavity patterns.

The new log K MA were additionally introduced in WHAM 6 to predict speciation of the World Average River Water. REE occur predominantly as organic complexes (REE-HM %) in circumneutral pH waters (pH range between 5-5.5 and 7.5-8). New fitted constants allow Model VI to calculate high proportions of LREE complexed with organic matter under alkaline pH conditions. Overall, the results of this study suggest that further considerations about organic matter should be taken into account to model and thus better understand global continent-ocean trace metal cycles. [START_REF] Goldstein | Rare earth elements in river waters[END_REF][START_REF] Elderfield | The rare earth elements in rivers, estuaries, and coastal seas and their significance to the composition of ocean waters[END_REF][START_REF] Sholkovitz | The aquatic chemistry of rare earth elements in rivers and estuaries[END_REF][START_REF] Deberdt | New insights about the rare earth elements (REE) mobility in river waters[END_REF], and (c) soil waters showing MREE enrichment [START_REF] Viers | Chemical weathering in the drainage basin of a tropical watershed (Nsimi-Zoetele site, Cameroon): comparison between organic-poor and organic-rich waters[END_REF][START_REF] Dia | The distribution of rare earth elements in groundwaters: assessing the role of source-rock composition, redox changes and colloidal particle[END_REF][START_REF] Gruau | Controls on the distribution of rare earth elements in shallow groundwaters[END_REF]. for comparison purpose [START_REF] Thurman | Organic Geochemistry of Natural Waters[END_REF]). Table 1. Model VI parameters for humic acid [START_REF] Tipping | Humic Ion-Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF].

Figure, Table and Appendix Captions

Table 2. Log K MA values obtained for the three datasets using Model VI. rmse refers to root mean square error of the regression analysis (see text for details) while sd refers to standard deviation on the mean. Fig. 6. ) Fig. 7.

[HA] (mg L

  electrostatic interactions. There is an empirical relation between net humic charge and an electrostatic interaction factor. The discrete binding sites are represented by two types of sites (types A and B), each comprising four different site subtypes present in equal amounts. Types A and B sites are described by intrinsic proton binding constants (pK A and pK B ) and spreads RI WKH YDOXHV S. A DQG S. B ) within each type. There are n A (mol g -1 ) Type A sites (associated with carboxylic type groups) and n B = n A /2 (mol g -1 ) Type B sites (often associated with phenolic type groups). Metal binding occurs at single proton binding sites (monodentate complexation) or by bidentate complexation between pairs of sites. A proximity factor is introduced that quantifies whether pairs of proton binding groups are close enough to form bidentate sites. Type A and Type B sites have separate intrinsic binding constants (log K MA and log K MB ), together asVRFLDWHG ZLWK D SDUDPHWHU /. 1 , defining the spreads of values DURXQG WKH PHGLDQV $ IXUWKHU SDUDPHWHU /. 2 , takes into account a small number of stronger sites (bidentate and tridentate sites).

  Fig. 1 and Fig. 2. &DOFXODWLQJ ORJ . ¢¡ YDOXHV Calculations were performed using the computer program WHAM 6 (Version 6.0.13) which includes Model VI. The three datasets corresponding to the three different HA concentrations used were separately modelled. A classical strategy that consists in adjusting log K MA for each REE simultaneously until a reasonable fit is achieved was used. Values of [REE 3+ ] (i.e., the concentration of REE left in the < 5 kDa ultrafiltrates) DQG PROHV RI 5(( bound per gram of humic acid; i.e., the difference between the concentration of REE left in the < 5 kDa ultrafiltrates and the concentration of REE initially present in the experimental solutions) are derived from the experimental data. The best fit is obtained when the root mean square error of the regression (rmse = sum of the squares of the differences between observed DQG FDOFXODWHG ORJ LV PLQLPLVHG 7KH three sets of optimised log K MA values thus obtained are reported in Table 2 for the 14 analyzed REE and illustrated in Fig. 3 for the case of La. As indicated by rmse values (< 0.07), fits could be considered of good quality. The main characteristics of the REE-HA experiments to be reproduced by Model VI are a high proportion of REE-HA complexes at relatively low pH and a marked increase of this

  mimicking both those obtained by[START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF][START_REF] Yamamoto | Interpretation of REE patterns in natural water based on the stability constants[END_REF] for SRFA and SRHA and through this study for AHA. +RZHYHU LQ VRPH VDPSOH SDUWLFXODUO\ DW S+ ORJ 5((-)$ VKRZ ERWK D 05(( GRZQZDUG FRQFDYLW\ VXSHULPSRVHG RQ DQ RYHUDOO LQFUHDVH LQ ORJ (REE-FA) from LREE to HREE (Yamamoto et al. 2006). Location of Fig. 5.

  actually reflect a pattern showing a continuous increase of the constant values from La to Lu, and thus a real lanthanide contraction effect. However, it may also reflect a REE pattern showing an increase of log β values only from La to Sm or Eu and then constant values from Gd to Lu (no real HREE enrichment). It may even reflect a MREE downward concave pattern, the only required feature in that case being log β La < log β Lu . To test this possibility (Gd/Lu) ratios (i.e., shape of the MREE and HREE part of the constant pattern) for the same 101 organic compounds were calculated and compared with (Gd/Yb) ratios and log RI WKHVH compounds. As shown in Fig.5, the comparison shows that the positive correlation reported by[START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF] when reporting (La/Lu) ratios of 101 compounds against their ave is lost when (Gd/Yb) is substituted to (La/Lu) ratios. For the purpose comparison Yamamoto et al. data for SRFA and SRHA[START_REF] Yamamoto | Systematics of stability constants of fulvate complexes with rare earth ions[END_REF][START_REF] Sonke | Lanthanide-humic substances complexation. II. Calibration of Humic Ion-Binding Model V[END_REF] and Sonke and Salters' data for SRFA and LHA (2006) were included in Fig. 5. In the present study, preliminary REE-HA conVWDQWV FDOFXODWLRQV QRW VKRZQ JLYH DQ RUGHU RI PDJQLWXGH IRU ORJ values. While <DPDPRWRV ORJ IRU 65)$ DQG 65+$ DQG RXU ORJ IRU $+$ are in the field of natural carboxylic and phenolic acids (Fig. 5), 6RQNH DQG 6DOWHUV ORJ IRU 65)$ DQG /+$ are in the field of natural phenolic acids, natural aminocarboxylic acid and synthetic

Fig. 1 .

 1 Fig. 1. Proportions of (a) La-HA, (b) Eu-HA and (c) Lu-HA complexes as a function of pH

Fig. 5 .

 5 Fig. 5. Literature compilation of REE-organic ligand constants (recalculated at , = 0.1 M when necessary;[START_REF] Wood | The aqueous geochemistry of the rare-earth elements: Critical stability constants for complexes with simple carboxylic acids at 25°C and 1 bar and their application to nuclear waste management[END_REF][START_REF] Byrne | Comparative complexation behavior of the rare earths[END_REF]. black triangles: amino-carboxylic acids;

Fig. 6 .

 6 Fig.6. REE normalized (to UCC)[START_REF] Taylor | The Continental Crust: Its composition and evolution[END_REF] patterns of river waters

Fig. 7 .

 7 Fig. 7. (Gd/Yb) UCC ratios as a function of DOC concentrations; data are from river waters

Fig. 8 .

 8 Fig. 8. Model VI speciation calculations for (a) La, (b) Eu and (c) Lu in World Average River

  Fig. 1.

Fig. 2

 2 Fig.2

  Fig. 3.

Table 2 .

 2 Log K MA values increase from La (2.58) to Eu (2.65), then decrease from Gd (2.63) to Lu (2.59). It follows a log K MA pattern showing a MREE downward concavity.

Table 3 .

 3 Modelled log K MA values for Tb (2.57) and Eu (2.73 and 2.77) are within the error range of the average log K MA values obtained here for these two REE (2.62 ± 0.16 and 2.65 ± 0.15, respectively; see Table2). Only results for Dy are statistically different: 3.19 against 2.61 ± 0.16 in the present study. The present results have also been compared to recently published results for the complexation of the 14 naturally occurring REE by Leonardite HA (LHA), a coal derived HA[START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF]. Complexation of the REE by LHA was experimentally studied at four different pH (6, 7, 8 and 9) using an EDTA-ligand competition method[START_REF] Sonke | Lanthanide-humic substances complexation. I. Experimental evidence for a lanthanide contraction effect[END_REF]. The corresponding modelled log K MA values are reported in Table4. Even if log K MA values calculated for the LHA are of the same order of magnitude than log K MA values calculated for the AHA, a marked difference is noted in the REE fractionation pattern of log K MA values. REE-LHA complexation constants do not evidence

the same MREE downward concavity as compared to that obtained with AHA, showing instead a progressive and continuous increase of log K MA values from La to Lu (i.e., log

Table 3 .

 3 Experimental conditions and fit of literature data (IE: ion exchange; S:

	spectrofluorometry). rmse corresponds to root mean square error of the regression analysis
	(see text for detail).

Table 4 .

 4 Log K MA values obtained for the fourSonke and Salters' datasets (2006) using Model VI (pH 6 to 9, 0.1 mol L -1 NaNO 3 , 100 nmol L -1 REE, 10 mg L -1 LHA and 500 nmol L -1

	EDTA).
	Appendix A. Proportion of REE-HA.

  La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu

	a	100
			80	pH
	% Ln bound to HA		20 40 60	2.47 2.95 4.06 4.25 4.35 4.99 6.29 10.44
			0
	b		100
			80	pH
				2.18
	% Ln bound to HA	40 60	2.91 3.45 3.96 4.40 6.16 6.66 6.94
			20	7.26
			0
			La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
	c		100
				pH
			80	2.21
				2.46
	% Ln bound to HA	40 60	2.97 3.51 4.05 5.03 5.48 6.08 6.25
			20	6.65
				6.77
			0
			La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu

  La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu

		1E-5	(a)
	Concentration/Upper Crust	1E-7 1E-6		Mississippi Indus Delaware Pampamga
				Vasterdalaven
				Tohibetsu
			(b)
	Concentration/Upper Crust	1E-6 1E-5		Amazon RioNegro Ntalla Orinoco Caroni
				Connecticut
				Mengong
		1E-7
			La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
			(c)	Mercy
				LeHome
				Mar2
	Concentration/Upper Crust	1E-4 1E-3
		1E-5
			La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu

Table 3

 3

$FNQRZOHGJHPHQWV We thank the technical staff at Rennes (M. Le Coz-Bouhnik, O. Hénin and P. Petitjean) for their assistance during the experimental and analytical work. Dr. R. E. Martinez is acknowledged for useful discussions and English corrections. Dr. J.E. Sonke and an anonymous reviewer are thanked for thorough and constructive comments of an earlier version of this paper. This research was supported by the CPER programs 'pYHORSSHPHQW GH OD 5HFKHUFKH VXU OD 0DvWULVH GH OD 4XDOLWp GH O(DX HQ %UHWDJQH jointly funded by the French Government and the Council of Rennes Métropole.

Up to now investigations of REE speciation in natural river and soil waters were performed using Model V and Model VI with extrapolated constants obtained from Linear Free-Energy Relationships (LFER) [START_REF] Tang | Speciation of rare earth elements in natural terrestrial waters: Assessing the role of dissolved organic matter from the modeling approach[END_REF][START_REF] Pourret | Organic complexation of rare earth elements in natural waters: Evaluating model calculations from ultrafiltration data[END_REF] and using Model V with constants fitted from experimental complexation between REE and HM [START_REF] Sonke | Lanthanide-humic substances complexation. II. Calibration of Humic Ion-Binding Model V[END_REF]. Using the new determined log K MA dataset, REE speciation calculations -as a function of pH -were performed for the World Average River Water [START_REF] Tang | Speciation of rare earth elements in natural terrestrial waters: Assessing the role of dissolved organic matter from the modeling approach[END_REF] in order to validate the results obtained with extrapolated constants. Major ions such as Fe and Al were adopted for re-calculating the REE speciation of the modelled World Average River Water (see Table 11 in [START_REF] Tang | Speciation of rare earth elements in natural terrestrial waters: Assessing the role of dissolved organic matter from the modeling approach[END_REF]. We also followed their assumption that 80 % of the active HM in this sample is present as FA and 20% as HA. Thus, the DOM content of World Average River Water is 10 mg L -1 , of which only 5 mg L -1 consist of HM able to complex with the REE, with 4 mg L -1 (80 %) present as FA, and 1 mg L -1 (20 %) as HA. Since pH is a key parameter to REE speciation, the REE speciation calculations were performed in the same way of these authors (op. cit.), i.e., holding the major solute composition constant (except carbonate alkalinity varying as a function of pH). Moreover, a fair degree of similarity between the model constants obtained for fulvic and humic acids exists as previously stated [START_REF] Tipping | A unifying model of cation binding by humic substances[END_REF][START_REF] Tipping | Humic Ion-Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF] so log K MA (FA) were calculated from log K MA (HA), obtained in section 3, using Eq. 19 in [START_REF] Tipping | Humic Ion-Binding Model VI: an improved description of the interactions of protons and metal ions with humic substances[END_REF] [START_REF] Tang | Speciation of rare earth elements in natural terrestrial waters: Assessing the role of dissolved organic matter from the modeling approach[END_REF][START_REF] Sonke | Lanthanide-humic substances complexation. II. Calibration of Humic Ion-Binding Model V[END_REF][START_REF] Pourret | Organic complexation of rare earth elements in natural waters: Evaluating model calculations from ultrafiltration data[END_REF]


Sunit Sivasankaran
email: sunit.sivasankaran@inria.fr

Brij Mohan

Lal Srivastava

Sunayana Sitaram
email: susitara@microsoft.com

Kalika Bali
email: kalikab@microsoft.com

Monojit Choudhury
email: monojitc@microsoft.com

Phone Merging for Code-switched Speech Recognition

Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of codeswitched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2% WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.

Introduction

Multilingual speakers tend to alternate between several languages within a conversation. This phenomenon is referred to as code-switching (CS). Automatic Speech Recognition for CS speech is challenging. Code-switched speech recognition present challenges in acoustic, language and pronunciation modeling of speech. Acoustic Mod- * This work was done while interning at Microsoft Research-India els (AMs) need to model phones in a mixed language setting, where co-articulation effects from one language may influence the other. Moreover, language models needs to be capable of predicting code-switch points between the two languages. The vocabulary size may be double of what is present in monolingual systems. Accents and native language influence may pose challenges to pronunciation models. Another major challenge in building code-switched ASR is the lack of data for different language-pairs. To curb the issue of contextual data availability per phone, we study the effect of manual merging and two automatic merging over the performance of Hindi-English codeswitched speech recognition system.

In systems with a small amount of data for training the AMs, phones that are similar to each other in the two languages being mixed can be merged, leading to more data for each phone. This may be especially useful in the case of related languages, or when a strong native language accent is expected to influence pronunciations in the other language. We experiment with phone merging in AMs of Hindi-English code-switched conversational speech, and show that we can get improvements on Word Error Rate (WER) by merging certain phones.

One technique to merge phones in the two languages being mixed is to use a common phoneset such as the International Phonetic Alphabet (IPA), or knowledge from a bilingual speaker to decide which phones can be merged (manualmerge). However, this may not always find the optimal merges, particularly if the phoneset we are starting with is not the appropriate representation for the dialect present in the speech. Another technique is to automatically find candidate merges by taking into account phone errors made by the ASR system in presence of a monolingual context (data-driven). Thirdly, we can create lex-icons with all possible pronunciation variants covering all candidate phones and allow the decoder to choose the correct pronunciation variant during decoding (probabilistic). We implement and discuss these techniques for phone merging in Hindi-English code-switched speech recognition.

The paper is organized as follows. Section 2 relates this work to prior work in code-switched speech recognition. Section 3 describes the Hindi-English speech data that we used. We describe our proposed techniques and experiments with phone merging in Section 4 and conclude the paper in Section 5.

Relation to Prior Work

Code-switched speech recognition has been studied in the context of acoustic, language and pronunciation modeling. The Language Identification (LID) based approach is to identify the language boundaries and subsequently use an appropriate monolingual ASR system to recognize monolingual fragments [START_REF] Joyce Yc Chan | Detection of language boundary in code-switching utterances by bi-phone probabilities[END_REF] or run multiple recognizers in parallel with an LID system [START_REF] Basem | Automatic speech recognition of code switching speech using 1-best rescoring[END_REF][START_REF] Weiner | Integration of language identification into a recognition system for spoken conversations containing code-switches[END_REF]). Another approach is to train an AM on bilingual data [START_REF] Lyu | Speech recognition on code-switching among the chinese dialects[END_REF][START_REF] Vu | A first speech recognition system for mandarinenglish code-switch conversational speech[END_REF] or to use one of the monolingual AMs [START_REF] Bhuvanagirir | Mixed language speech recognition without explicit identification of language[END_REF] or to pool the existing monolingual AMs by sharing phones belonging to both languages. [START_REF] Yeh | An improved framework for recognizing highly imbalanced bilingual code-switched lectures with crosslanguage acoustic modeling and frame-level language identification[END_REF] tackle the problem of code-switching in which a speaker speaks mainly in one language, leading to an imbalance in the amount of data available in the two languages, with cross-lingual data sharing approaches. [START_REF] Pandey | Adapting monolingual resources for code-mixed hindi-english speech recognition[END_REF] also propose studies to adapt matrix language (monolingual Hindi) resource to build better code-mixed acoustic model in case of read speech.

Yilmaz et al. [START_REF] Yılmaz | Investigating bilingual deep neural networks for automatic recognition of codeswitching frisian speech[END_REF] describe two DNN architectures for recognizing Frisian-Dutch code-switched speech. They use language dependent phones in which each phone is tagged with the language and modeled separately. They also use language independent phones by modeling them jointly and merging them on the basis of the associated IPA symbol. In their experiments, they find that the language dependent approach performs better. [START_REF] Lyudovyk | Code-switching speech recognition for closely related languages[END_REF] describe an approach for code-switched speech recognition of closely related languages, namely, Ukrainian and Russian by creating a bilingual pro-nunciation lexicon. [START_REF] Joyce Yc Chan | Automatic recognition of cantonese-english code-mixing speech[END_REF] describe a two pass approach for Cantonese-English mixed speech recognition, in which they develop a cross-lingual phonetic AM, with the phone set designed based on linguistic knowledge. [START_REF] Yu | Chinese-english bilingual phone modeling for crosslanguage speech recognition[END_REF] present three approaches for bilingual phone modeling for Mandarin-English speech, namely combining phone inventories, use IPA mappings to construct a bilingual phone set and clustering phones with hierarchical clustering by using the Bhattacharyya distance and the acoustic likelihood. The third approach outperforms the IPA-based mapping and is comparable to the combination of the phone inventories.

A closely related area of research is the Multi-Lingual speech recognition [START_REF] Toshniwal | Multilingual speech recognition with a single end-to-end model[END_REF]Schultz andWaibel, 1997, 2001;[START_REF] Lin | A study on multilingual acoustic modeling for large vocabulary asr[END_REF][START_REF] Vu | Multilingual deep neural network based acoustic modeling for rapid language adaptation[END_REF]. Though the problems in multi-lingual ASR and ASR for code-switched data seem similar such as; large phonetic space due to the incorporation of the phones of both languages, code-switched ASR has its own set of challenges. For example, even with a large corpus, getting enough data at code-switched points for both the Acoustic Model and the Language Models is very challenging.

Data

The dataset used in this work contains conversational speech recordings spoken in code-switched Hindi and English. Hindi-English bilinguals were given a topic and asked to have a conversation about the topic with another bilingual. They were not explicitly asked to code-switch during recording, but around 40% of the data contains at least one English word in an utterance. The conversations were transcribed by bilingual transcribers. Hindi words were transcribed in the native Hindi script Devanagari, and English words in Roman script. There was no distinction made between borrowed words and code-switching, which led to some inconsistencies in transcription. Each conversation was broken down into utterances ranging from very short one word utterances to long sentences.

A summary of the Hindi-English code switched dataset used in our experiments is shown in Table 1. The code-switching statistics mentioned are particular to this dataset and is subject to change depending on the speaker. However, the phone

Phone merging

In this section, we first describe the baseline ASR system built by combining the Hindi and English phonesets, followed by experiments conducted on phone merging and the resulting impact on Word Error Rate (WER). All experiments were carried out using Kaldi [START_REF] Povey | The kaldi speech recognition toolkit[END_REF] and the phone merging techniques are implemented in Python.

Baseline ASR System

The CMU Pronunciation dictionary (Weide, 1998) containing 39 phones was used as the lexicon for all the English words in the data. This is not the most appropriate choice given that all the speakers in the data speak Indian English, however, due to the lack of a publicly available large Indian English lexicon, we used CMUdict. To generate the lexicon for the Hindi words, we used the Festvox Indic front end (Parlikar et al.), which does basic grapheme to phone conversion for Hindi, including schwa deletion and contextual nasalization. The Hindi phoneset consisted of 50 phones after removing some very low frequency phones. We used Feature-space maximum likelihood linear regression (fMLLR) [START_REF] Mark | Maximum likelihood linear transformations for hmm-based speech recognition[END_REF] to train a context-dependent GMM-HMM acoustic model and a trigram Language Model (LM) built on the transcripts during decoding. With this system, we obtained a baseline Word Error Rate (WER) of 40.36%.

We evaluated the accuracies at phone level by comparing the transition arcs that correspond to phones in the decoded lattices with the alignments obtained from the GMM-HMM model as ground truth. Figure 1 shows a scatter plot of the phone accuracy with respect to the log of the data available per phone in the test dataset. Evidently, we observe higher accuracies for phones with larger count with a few exceptions such as, /nN HP/, /zh/ and /dR HP/.

Manually merging similar sounding phones

To increase the data availability per phone, we merged similar sounds in both languages even if they are not exactly the same linguistically (in terms of their articulation). The mapping between Hindi phones and CMUdict phones in the Festvox Indic frontend, built for enabling a Hindi TTS system to pronounce English words [START_REF] Sitaram | Speech synthesis of code-mixed text[END_REF], was used for this purpose. All the merged phones (a total of 31) were prefixed with "-HP-M".

To analyze the impact of merging, we started by merging a pair of phones -the English phone eh (example "academic ae k ah d eh m ih k") with a similar sounding Hindi phone E-HP (example in Roman script: "kehana k-HP E-HP hv-HP nB-HP Aa-HP"). This resulted in 38 English phones, 49 Hindi phones and 1 merged phone resulting in 88 unique phones. We obtained a WER of 40.21 using a GMM-HMM acoustic model, which is a negligible improvement over the system with no merging. The bar plot in Figure 2 shows the change in the accuracies for each phones with respect to the baseline. We notice an improvement of 4.18% and 4.5% in the accuracy of the 'eh' and 'E-HP' phones respectively. Similar performance was obtained while merging the English phone n with the Hindi phone nB-HP. In both cases, a decrease in accuracies for a few phones were observed.

Then, we merged all the 31 pairs of similar sounding English and Hindi phones. We refer to this system as the "all merged" system, for which we obtained a WER of 39.7%. There was a noticeable improvement in the accuracies of about 75% of the phones as shown in Figure 3 . We observed a decrease in accuracies for phones which do not have similar sounding equivalents to merge, such as the Hindi phones 'sr-HP' and 'nN-HP' and the English phone 'ng'. Large improvements in the phone accuracies, amounting to around 50%, were observed in merged phones such as 'ow-HP' (merged with the English phone 'ao') and 'tr-HP' (merged with the English phone 't'). Conspicuously, the highest improvements were for phones with low count prior to merging.

Measuring phone accuracy changes

To evaluate the performance of these systems in terms of phone accuracy, we computed the weighted average of change in accuracies. The weights correspond to the data available per phone.

We measure Weighted Average of Phoneme Improvement (henceforth referred to as W AP I) as:

W AP I = i w i × ∆acc i i w i
, where w i and ∆acc i represent the data available per phone and change in phone accuracy with respect to the baseline respectively. A summary of the W AP I score for different acoustic models is shown in Table 2. The "all merged" system gives the highest WAPI score of 2.45%, whereas merging 'eh' with 'E-HP' and 'n' with 'nB-HP' results in a WAPI score of 0.31 and 0.47 respectively. Interestingly merging both 'eh' with 'E-HP' and 'n' with 'nB-HP' results in WAPI score of 0.49 which is higher compared to individually merging these phones.

From the above experiments we infer that merging phones result in more data for data-starved phones which in turn improves the phone accuracies.

Phones

Improved Acoustic Models

We also performed the same experiments using two Deep Neural Network (DNN) based acoustic models. The first model was built using 5 hidden layers with p-norm [START_REF] Zhang | Improving deep neural network acoustic models using generalized maxout networks[END_REF] as the non-linearity. The input dimension of each hidden layer was 5000 and output dimension was 500. We used 40 dimensional MFCC along with 4 splicing frames on each side, appended with 100 dimensional i-vectors [START_REF] Dehak | Front-end factor analysis for speaker verification[END_REF] as input features. We also built a time-delay neural network (TDNN) [START_REF] Peddinti | A time delay neural network architecture for efficient modeling of long temporal contexts[END_REF] with 6 hidden layers and ReLU as the non linearity. Figure 3: Change in phone accuracies after merging all similar sounding Hindi and English phones.

Motivated by this improved ASR performance, we investigate approaches to merge phones using data driven methods. We hypothesize that the data driven methods will provide us with clues on the acoustic similarity between phones to be merged.

Data-driven phone merging (DDPM)

The potential phone pairs to merge can be identified based on the errors made by the decoder, with respect to the alignments. A TDNN acoustic model trained using the unmerged (89 phones) phoneset was used to decode the utterances from the dev dataset, with a low LM weight of 1 so as to minimize the influence of LM. Phoneme sequences are then derived by parsing the best path through the decoded lattice. The same acoustic model is also used to align the dev data and ob-tain the corresponding true phone sequences and their durations. We choose pairs of aligned and decoded utterances with a strict threshold of 80% or more overlap in duration. Using the alignments, we identify English phones which were wrongly decoded as Hindi phones. We call them crosslanguage swaps. Frequent swapping between English-Hindi phone pair, indicates the need for the pair to be merged.

Using this approach we observe several merges that were present in the manual merge. For instance, (e HP, ey), (A HP, ah), (ii HP, iy), (m HP, m), (b HP, b), (ph HP, f). Errors such as, (Aa HP, l), (u HP, ah), (j HP, ey), (g HP, l), were also noticed. WER of 52.95, 45.99 and 42.16 were obtained using GMM, DNN and TDNN models respectively, after merging the phones identified by the data-driven method. The decrease in performance can be attributed to the wrong phone swaps.

Inducing context sensitivity through reliability coefficients

Spurious phone swaps degrade the performance of the ASR substantially. This can be reduced by taking into account the phone context. We associate a notion of context reliability with each context c, which is defined as the proportion of correct within-language phone predictions by the decoder out of all the instances of a context c. We compute this reliability as a coefficient (α c ) for left, right and both contexts in two different ways. α c can be computed with respect to a specific center phone (α c (p)) as:

α c (p) = correct instances of p with c total instances of p with c = P (x = p|c) (1)
An alternate method referred to as global context reliability coefficient, is to compute the context reliability coefficient for every context irrespective of the center phone p. This is obtained by computing the ratio of the counts of the correct instances for any arbitrary phone in presence of context c to the total instances of context c:

α c = correct instances of context c total instances of context c (2)
The computed α c applied as weights while combining the probability of cross-language swaps conditioned on the context c.

Our goal is to compute the conditional probability of the decoded phone(x d ) given the alignment phone (x a ), which is P (x d |x a ). The phone ( xd ) with the highest probability will be chosen as the potential merge for x a (eq. 3).

xd = arg max

x d P (x d |x a ) (3)
The context information is incorporated by computing the conditional probability specific to a context as P (x d |x a , c) and then marginalizing over all possible c to obtain the swap probability P (x d |x a ) (eq. 4).

P (x d |x a ) = c P (x d |x a , c)P (c|x a ) ≈ c P (x d |x a , c)P (x a |c)P (c) ≈ c P (x d |x a , c)α c P (c) (4)
P (c) is the prior probability for each context c which is computed using the dev dataset. We assign a neutral reliability (α 0 ) score of 0.01 and prior P (c) of 0.01 to all the unseen contexts. The min and max values of α c are 0.0 and 1.0 respectively.

Figure 4 shows cross-language phone confusion matrices for the two context-sensitive data driven phone merging approaches. We observe that phone-specific coefficients are able to capture only the most prominent merges while global coefficients produce merges that highly correlate with the manual merge. This might be due to the division of context information across phones which reduces the context sensitivity. This clearly suggests that some contexts help in producing better predictions than others, regardless of the reference phone. Although many of the swaps predicted using DDPM closely resemble the manual merges, the manual merge method outperformed global-DDPM by approx. 3% absolute as seen in Table 3. The distribution of α c values show that the left and the right context exhibit high confidence scores, whereas low confidence scores were observed when both the context were considered. Hence, we will benefit by removing the spurious low-confidence contexts while merging.

Figure 5 presents the swap likelihood of the predicted phone-pairs in decreasing order. We observe that phone pairs that have the highest swap likelihoods include nasals, close vowels and stops. It is interesting that the data driven method identified new phone merges such as (ae, Aa HP) and (aa, ow HP) compared to manual merged phones. We believe that incorporating these new phone merges into the manually merged phone set will improve the ASR performance. Further experiments need to be conducted to verify this claim.

Probabilistic merging

Next, we propose a method to allow the acoustic model to select appropriate phones during decoding. We trained an acoustic model using the merged phones while also retaining the Hindi and English phones. For example, the phone set for the new AM contained the English phone 'eh', the Hindi phone 'E-HP' as well as the merged phone 'E-HP-M'. The intuition behind this approach is to let the decoder choose between multiple pronunciation variants in the lexicon so as to determine the pronunciation used by a speaker who code- switched. This approach has been shown to work well in speech synthesis for pronunciation modeling of homographs [START_REF] Sitaram | Using acoustics to improve pronunciation for synthesis of low resource languages[END_REF]. Table 4 compares the number of unique Hindi, English and merged phones for the different systems mentioned.

During training, we modified the lexicon so as to retain part of the data for the unmerged phones and assigned the rest to the merged phones. We assigned 30% and 70% of the data to merged phones which we refer to as P 30 and P 70 . During decoding, we created a different lexicon allowing all possible pronunciation variants. We obtained WERs of 41.21% and 40.92% using the HMM-GMM based AM for P 30 and P 70 models respectively which are lower than the baseline. WAPI 4 shows the number of English, Hindi and merged phones for each technique.

The WER is a function of number of insertion, deletion and substitution errors as well as the correct token numbers. Figure 6 shows the relative percentage change in the insertion, deletion and substitution values of the " all merge", P 30 and P 70 model compared to the baseline system using TDNN as AM. The best system should have the lowest insertion, deletion and substitution errors and highest correct tokens. The "all merge" model, which has the best WER scores, has higher insertion and substitution errors but performs better on deletion errors and recognition of correct tokens. We can infer that certain phone merges are causing higher insertion and substitution errors and should be avoided, thus concluding that the manually merged phones are sub-optimal.

Conclusion

In this work, we compare phone merging techniques in context of code-mixed Hindi-English speech with a baseline system built using a union of both phone sets. We first merge similar sounding phones across both languages manually in order to reduce the phone set size and increase the data availability per phone. We observe a 3% relative improvement in the WER values compared to the baseline using a p-norm based DNN model along with a significant improvement in phone accuracies. We then propose data-driven approaches to merge phones automatically. To correct the errors made by data-driven method, we weight the cross-language swaps using reliable withinlanguage contexts. These methods gave newer phone merge recommendations which can be useful to improve the ASR performance. We further propose probabilistic methods where in the decoder is provided with both the merged as well as the unmerged phones which reduced the insertion errors compared to the manually merged system. these techniques came close to, but did not improve upon the manually merged ASR system. Error analysis of manual merging indicates that it is not optimal and there is a need for better datadriven techniques to automatically merge phones for code-switched ASR.

Figure 1 :

 1 Figure 1: Scatter plot of log of data count per phone with respect to the phone accuracy on the test set.

Figure 5 :

 5 Figure 5: Swap-pair likelihood for phone-specific (α c (p)) and global (α c ) context reliability coefficients.

Figure 6 :

 6 Figure6: Percentage change in insertion, deletion, substitution and correct token recognition numbers compared to the baseline system with TDNN as AM. The best system should have the lowest of insertion, substitution and deletion numbers and the highest of correct token numbers. The number of word tokens in baseline system for insertion, deletion, substitution and correct tokens are 3342, 5735, 11089 and 49011 respectively.

Table 2 :

 2 Weighted average of phone improvement (WAPI) scores for different phone merging.

	Merged	WAPI
	All	2.45
	eh with E-HP	0.31
	n with nB-HP	0.47
	eh with E-HP & n with nB-HP	0.49
	P 30	-0.59
	P 70	1.63

Table 4 :

 4 Number of phones before and after merging score of 1.63 for P 70 model was higher compared to -0.59 of P 30 model but lower compared to the all merge model. Table

		En Hi Merged Total
	Baseline	39 50	0	89
	All merge	8 19	31	58
	DDPM	9 20	30	59
	DDPM (α c (p)) 20 25	14	59
	DDPM (α c )	21 24	14	59
	Probabilistic Merging	39 50	31	120

Acknowledgments

We thank the anonymous reviewers for carefully reading our manuscript and offering valuable suggestions.