X Artru
email: x.artru@ipnl.in2p3.fr‡z.belghobsi@univ-jijel.dz

Z Belghobsi

THEORETICAL CONSIDERATIONS FOR A JET SIMULATION WITH SPIN 1

A first part lists basic rules, taken from the string-and multiperipheral models, that a recursive quark fragmentation model should obey. A second part describes spin effects given by the classical "string + 3P0" mechanism of quark-antiquark pair creation, in pseudoscalar and vector meson production: Collins effect, jet handedness and "hidden spin" effects in unpolarized experiments. The last part constructs a recursive quantum-mechanical model of spin-dependent fragmentation. In a "ab initio" approach an integral equation must be solved as a preliminary task. With a "renormalized input", this task is reduced to an ordinary integration. A spindependent generalization of the symmetric Lund model is obtained.

Introduction

A jet model which takes into account the quark spin degree of freedom must start with quantum amplitudes rather than probabilities. A "toy model" [START_REF] Artru | Proc. of XIII Advanced Research Workshop on High Energy Spin Physics[END_REF] using Pauli spinors and inspired from the multiperipheral model and the classical string + 3 P 0 mechanism [START_REF] Andersson | [END_REF]3] followed this principle. Collins-and longitudinal jet handedness [4] effects were generated. However hadron mass-shell constraints were ignored. These constraints are satisfied in an improved model [START_REF] Artru | Proc. of XIV Advanced Research Workshop on High Energy Spin Physics[END_REF], which is a symmetric-Lund model endowed with spin factors. In the ab initio approach of [START_REF] Artru | Proc. of XIV Advanced Research Workshop on High Energy Spin Physics[END_REF] the inputs are quark propagators and quark-hadron vertices derived from a string action. The recursive splitting function is obtained by solving an integral equation. We will show that, starting from a renormalized input, this preliminary task is replaced by an ordinary integration.

Section 2 lists the rules and approximations of a bona fide recursive jet model. Spin effects produced by the classical string + 3 P 0 mechanism or the "toy model" are sketched in Sec. 3. The next sections develop the model of Ref. [START_REF] Artru | Proc. of XIV Advanced Research Workshop on High Energy Spin Physics[END_REF] in three stages: the ab initio approach, the renormalized input approach and the application with string anplitudes.

Rules and approximations for a recursive model

We take the example of W ± decay into q A + qB and no gluon (lower part of Fig. 1-left) followed by a hadronisation into mesons and no baryon (upper part of Fig. 1

-left), q A + qB → h 1 + h 2 ... + h N .
(1) In the multiperipheral picture, (1) is decomposed in recursive quark splittings

p z p 0 W ± q A q N q 4 q 3 q 2 h 1 h 2 h 3 h N k A k 2 k N p 1 p 2 p N k( q B ) p W q B q B
q 1 → h 1 + q 2 , q 2 → h 2 + q 3 , • • • • • • q N → h N + q B , (2)
with q 1 ≡ q A ; h n is the meson of rank n ≤ N ; q B ≡ q N +1 is the charge conjugate of qB and "propagates backward in time".

Factorization. We assume the approximate probability convolution

P event ≃ dΩ dP(W ± → q A qB ) dΩ × P(q A + qB → h 1 + h 2 ... + h N ) . ( 3
)
P event is the exclusive N-particle distribution of the whole event. dP/dΩ is the angular distribution of the quark momentum k A in the W ± rest frame. The last factor is the exclusive N-particle distribution of reaction [START_REF] Artru | Proc. of XIII Advanced Research Workshop on High Energy Spin Physics[END_REF]. k A /|k A | = ẑ defines the jet axis. In a more rigorous approach the convolution should bear on the amplitudes. k A is an internal momentum of the loop diagram of Fig. 1-left and P event is a double integral: in k A for the amplitude and in k ′ A for the complex conjugate amplitude. Factorization (3) ignores the pure quantum-mechanical quantity k A -k ′ A .

Multiperipheral dynamics. Each splitting conserves 4-momentum:

k n = p n + k n+1 .
These relations are exhibited in the momentum diagram of Fig. 1-right. A basic ingredient of the multiperipheral model is the cutoff in the quark virtualities -k 2 . It implies:

• a cutoff in |k + k -| ≡ (k 0 + k z ) |k 0 -k z |, which insures the approximate ordering of h 1 , h 2 , • • • h N in
rapidity and the leading particle effect (or favored fragmentation).

• a cutoff in k T leading to the Local Compensation of Transverse Momenta (LCTM) [6]. It leads to a cutoff in p T of the hadrons 2,3 .

Q 3 O q 3 X X + z t Q 2 Q 1
Figure 2: Relation (4) between the quark momentum q 3 in the multiperipheral picture and the point Q 3 where the q 3 q3 pair is created in the classical string fragmentation model with m q = 0, k T = 0.

Ladder approximation. A same hadronic final state can be obtained with several multiperipheral diagrams which differ by permutations. In the ladder approximation the interferences between these diagrams are neglected. Most often only one diagram is important, the others having rank ordering too far from the rapidity ordering.

String dynamics. The same properties are found in the String Fragmentation Model. Fig. 2 represents the world sheet of the massive string or dart stretched by q A and qB and decaying into hadrons, in a classical 1+1 dimensional model with massless quarks. It is a particular type of quark multiperipheral model, if one orders the Q-corners according to the null-plane time variable X -= t -z and make the correspondance

4 t(Q n ) -t(O) = k z n /κ , z(Q n ) -z(O) = k 0 n /κ , (4)
where κ ≃ 1 GeV/fm is the string tension (hereafter we take κ = 1). For a string breaking point Q the condition that there is no other breaking in its past cone leads to the suppression of large (OQ

) 2 ≡ -k + k -by a factor exp -b |k + k -| (5)
where 2b is the string "fragility" in units κ = 1. Quarks with masses and transverse momenta are thought to be produced by a tunneling mechanism similar to the Schwinger one for e + e -creation in strong electric field. It provides the k

T cutoff factor exp[-π(m 2 q + k 2 T )/κ]. ( 6
)
3 Properties of the classical string +3 P 0 mechanism Fig. 3 depicts the decay of the dart as if all Q n where at equal time. Assuming that a q n qn pair is created at Q n in the 3 P 0 state and with zero 4-momentum, one predicts a correlation between the antiquark polarization Sn and transverse momentum kn,T : kn • (ẑ × Sn ) is positive. A similar effect is predicted in atomic physics [START_REF] Redouane-Salah | Conf. Proc[END_REF].

Case where h 1 , h 2 , ... are pseudoscalar mesons. In that case q n and qn+1 forming h n have antiparallel spins. Combined with the k•(ẑ× S) correlations it gives: -a Collins effect toward S 1 × ẑ for the "favored" meson h 1 , -Collins effects of alternate sides for the next mesons, -a large Collins effect for h 2 , -Relative Collins Effects (or IFF ) larger than from "single-Collins" + LCTM alone.

Case where h 1 is a leading vector meson. In a vector meson of linear polarization A (being known from the decay products), the q and q polarizations are symmetrical about the plane perpendicular to A (Fig. 3b). Let us consider a 1 rst -rank vector meson:

-if A ẑ the Collins asymmetry is opposite to that of a leading pseudoscalar meson,

-if A ⊥ ẑ the Collins asymmetry is in the azimuth 2φ(A) -φ(S 1 ) -π/2, -if both A z = 0 and A T = 0 and if q 1 is helicity-polarized, S 1z A z A• ẑ × p is positive.
This is a longitudinal jet-handeness [4] effect. These three effects are reproduced by the "toy model". They correspond respectively to lines 3, 5 and 6 of Eq.( 27) of [START_REF] Artru | Proc. of XIII Advanced Research Workshop on High Energy Spin Physics[END_REF]. On the average, the Collins effect is -1/3 that of the pseudoscalar meson [START_REF] Czyżewski | [END_REF].

Hidden spin effects. Whether q A is polarized or not, the k•(ẑ× S) correlation of the string + 3 P 0 mechanism has an impact on the p T distribution of the rank ≥ 2 mesons: -for a pseudoscalar meson, p 2 T meson > 2 k 2 T quark , -for a vector meson linearly polarized along ẑ, p 2 T meson < 2 k 2 T quark , -for a vector meson linearly polarized along x, p 2

x < 2 k 2 T < p 2 y . On the average, p 2 T V-meson < p 2 T PS-meson . These "hidden spin" effects allow an unexpensive test of the string + 3 P 0 mechanism (note that the Schwinger mechanism predicts no k•(ẑ× S) correlation [9]). At least they suggest that quark spin plays a role even in unpolarized experiments and should be included in any jet model.

The ab initio approach

The starting point is the multiperipheral hadronization amplitude |k B , s B is the negative energy state whose hole is |k(q B ), s(q B ) . Inside curly brakets, {q} = (f, k) gathers the quark flavor f and 4-momentum k. For a meson {h} = (h, p, s h ) gathers the species h, the 4-momentum and the spin state. The quark propagator D{q} ≡ D(f, k) and the vertex function

k B , s B |M N {q A qB → h 1 h 2 • • • h N }|k A , s A = k B , s B | D{q B } V{q B , h N , q N } • • • D{q 3 } V{q 3 , h 2 , q 2 } D{q 2 } V{q 2 , h 1 , q A } D{q A } |k A , s A . (7) +k 3T k 3T k 2T k 2T q 1 q 3 q 2 L 2 L 3 q 3 q 2 q 4 L 4 A S 1
V{f ′ , h, f } ≡ V f ′ ,h,f (k ′ , k
) quark chain reversal or "left-right symmetry" [START_REF] Andersson | [END_REF], i.e., interchanging q A and qB . These invariances can be realized with Pauli spinors. For instance, we will take [START_REF] Artru | Proc. of XIII Advanced Research Workshop on High Energy Spin Physics[END_REF]

D{q} = D(f, k + k -, k 2 T ) (µ f + σ z σ • k T ) . (8)
Doing so, we do not take into account the whole information (2 q-bits) carried by an off-mass-shell Dirac spinor. We leave this question for further studies.

Hadronization "cross section" of quark q n . In the ladder approximation one can define the hadronization "cross section" of an initial or intermediate polarized quark q n ,

H{q B + ↑q n → X} = Tr R{q n } ρ{q n } , (9)
where ρ{q n } = (I + σ•S n )/2 is the spin density matrix of q n ,

R{q n } = 1 2 N ≥n d{h n } • • • d{h N } M † N -n M N -n δ 4 [p n + • • • + p N -k A -k(q B )] ( 10
)
and d{h} • • • stands for h s h d 3 p/p 0 • • • . We are interested in the q A fragmentation region, that is why we will took qB unpolarized. R{q} obeys the ladder integral equation (illustrated by Fig. 4):

R{q} = d{h} T † {q ′ , h, q} R{q ′ } T {q ′ , h, q} + h, s h M † 1 M 1 δ[(k -k B ) 2 -m 2 h ] (11)
with

T {q ′ , h, q} ≡ V{q ′ , h, q} D{q}. At large m 2 X ≃ |k - B | k + , R{q} ≃ B{q} (m 2 X ) α R , ( 12
)
B{q} = β(f, k 2 T ) [1 + A(f, k 2 T ) σ• ñ(k)] , (13)
with ñ(k) ≡ ẑ × k/|ẑ × k|. In ordinary multiperipheral models α R and B{q} are the intercept and residue of the output Regge trajectory. A(f, k 2 T ) is the single-spin asymmetry of ↑q + qB → X. A(f, 0) = 0. B{q} is semi-positive definite: β > 0, |A| ≤ 1.

Recursive Monte-Carlo algorithm. Suppose that we have already generated n-1 steps of ( 2) and recorded the density matrix ρ{q n }. The simulation of the next step ↑q n → h n + ↑q n+1 (hereafter rewritten ↑q → h+ ↑q ′ ) proceeds in two sub-steps:

(q 1 ) M N-1 q B …. …. q B q 2 q 1 T T † h 1 R(q 1 ) R(q 2 )
Figure 4: Ladder unitarity diagram associated to Eqs.(9-11) with n=1, q = q 1 , q ′ = q 2 . Black bullets represent quark propagators. The summation over N is understood.

1) generate the species and momentum of h. From Eqs. (11)(12) the type and momentum distribution of the next-rank particle is proportional to

dH{q B + ↑q} = dZ d 2 p T Z |k - B k ′ + | α R s h Tr B{q ′ } T {q ′ , h, q} ρ{q} T † {q ′ , h, q} , (14)
with

Z ≡ p + /k + , k ′ = k -p.
2) calculate the polarization of ↑q ′ . It is given by

ρ{q ′ } = s h T {q ′ , h, q} ρ{q} T † {q ′ , h, q} / Tr [idem] . (15)
If h has nonzero spin and one wants to simulate its decay, a more complicated algorithm is needed, following the rules of [11] (see also Sec.5.1 of [12]).

In this ab initio approach one must calculate α R and the functions β(f, k 2 T ) and A(f, k 2 T ) from the integral equation ( 11), as a preliminary numerical task.

The renormalized input approach

The physical properties (e.g., the multi-particle distributions) are unchanged by two kinds of "renormalization" of the propagators and vertices:

(a) new D{q} = |k -k + | λ D{q} , new V{q ′ ,h,q} = |k ′+ k -| λ V{q ′ ,h,q} (b) new D{q} = Λ{q} D{q} Λ{q} , new V{q ′ ,h,q} = Λ -1 {q ′ } V{q ′ ,h,q} Λ -1 {q} , (16)
where Λ{q} ≡ Λ(f, k T ) is a matrix in spin space. Under (these matrices commute). Then new α R = 0, new R{q} = I. Taking the renormalized V{q ′ ,h,q} as unique input, the renormalized propagator is obtained from (11):

D{q} = U -1 2 {q} with U{q} ≡ d{h} V † {q ′ , h, q} V{q ′ , h, q} . (17)
The preliminary task is now to evaluate (17). It is much easier than solving the integral equation (11). Besides, ( 14) is simplified by the absence of |k - B k ′ + | α R and B{q ′ }.

Application with string amplitudes

An ab initio string hadronization amplitude [START_REF] Artru | Proc. of XIV Advanced Research Workshop on High Energy Spin Physics[END_REF] can be expressed in the multiperipheral form with the propagator and vertex

D{q} = (k -k + -i0) α{q} exp (i -b) k -k + /2 d{q} , (18)
V{q ′ , h, q} = (p + /k ′+ ) α{q ′ } exp (b -i) k ′-k + /2 (-p -/k -) α{q} g{q ′ , h, q} . ( 19
) d{q} = d(f, k T ) and g{q ′ , h, q} = g f ′ ,h,f (k ′ T , k T )
are spin matrices and α{q} = α(f, k 2 T ). In the ladder approximation one can remove the phases of the exponential factors and of (k -k + -i0) α{q} . This does not change the probabilities. After renormalization,

V{q ′ , h, q} = (k ′+ /p + ) a{q ′ }/2 exp(b k + k ′-/2) (-k -/p -) a{q}/2 g{q ′ , h, q} , (20)
with a new g{q ′ ,h,q} and a{q} = old (α R -2 Re α{q}). The right Eq.( 17) becomes

U{q} = E(a{q}, -k -k + ) u{q} with E(a, x) ≡ x a e -bx , (21) u{q}
= h, s h d 2 p T dZ Z 1 -Z Z a{q ′ } E -a{q}, m 2 h + p 2 T Z g † {q ′ , h, q} g{q ′ , h, q} . (22)
Example: a{q} = constant and

g{q ′ , h, q} = e -B (k ′2 T +k T 2 ) (µ f ′ + σ z σ • k ′ T ) Γ (µ f + σ z σ • k T ) (23)
with Γ = σ z for a pseudoscalar meson and Γ = G L V * z I + G T σ•V * T σ z for a vector meson, like in the "toy model " [START_REF] Artru | Proc. of XIII Advanced Research Workshop on High Energy Spin Physics[END_REF]. A complex µ f with Imµ f > 0 reproduces the effects of the string + 3 P 0 mechanism.

The receipe (14-15) becomes 1. generate the species and momentum of h following the distribution

d 2 p T dZ Z 1 -Z Z a{q ′ } E -a{q}, m 2 h + p 2 T Z s h Tr t{q ′ , h, q} ρ{q} t † {q ′ , h, q} (24) with t{q ′ , h, q} = g{q ′ , h, q} u -1 2 {q}, 2. calculate the polarization of ↑q ′ with ρ{q ′ } = s h t{q ′ , h, q} ρ{q} t † {q ′ , h, q} / Tr idem . (25)
If quark spin is ignored, g{q ′ , h, q} = g f ′ ,h,f (k ′2 T , p 2 T , k 2 T ), u{q} = u(f, k 2 T ) and one recovers the symmetric Lund model. U{q} and j| V † {q ′ , h, q} |j ′ i ′ | V{q ′ , h, q} |i are the spindependent generalizations of ρ ν (V ) and ρ ν,ν ′ (V, V ′ ) in [10].

Conclusion

We have built a bona fide recursive quark fragmentation model including the quark spin degree of freedom. For pseudo-scalar and vector mesons the model can reproduce the Collins effects of the classical string + 3 P 0 mechanism and also give longitudinal jet handedness. It can be a guide for quark polarimetry and may also account for "hidden spin" effects in unpolarized quark fragmentation. The ab initio input consists in quark propagators and vertices. Using it, an integral equation has to be solved in order to fix the splitting distribution. Starting from the renormalized input, which consists in exponents a{q} = a(f, k 2 T ) and vertex matrices g{q ′ , h, q} = g f ′ ,h,f (k ′ T , k T ), only an ordinary integration is needed. Putting vertices derived from the semiclassical string action in 1+1 dimension, one obtains a spin-dependent generalization of the symmetric Lund model which may be implemented in a Monte-Carlo code of quark jet simulation.

Figure 1 :

 1 Figure 1: Left: quark-diagram of a hadronic decay of W ± . Right: associated momentum diagram, projected on the (p 0 , p z ) plane.

Figure 3 :

 3 Figure 3: (a) string decay into pseudoscalar mesons with the string+ 3 P 0 mechanism. (b) spin correlation of the quark and antiquark in a vector meson linearly polarized along A.

  ) are the inputs of the model. In a step-by-step covariant model, |k A , s A and |k B , s B would be Dirac spinors and D and V would be 4×4 matrices, e.g., D{q} = D(f, k 2 ) (m f + γ • k). However Lorentz covariance is required only globally for the whole process of Fig.1. Together with P and C conservation, this requires the invariance of M under -(a) rotations about ẑ, -(b) Lorentz transformations along ẑ, -(c) reflection about any plane containing ẑ, -

  (a) α R is shifted by 2λ. Under (b), new B{q} = Λ † {q} B{q} Λ{q}. Let us combine (a) and (b) with λ = -α R /2 and Λ = B -1 2 (D † /D) 1 4

Presented at XVI Advanced Research Workshop on High Energy Spin Physics (DSPIN-13)(Dubna, October 8-12,

2013)

The converse is not true: the p T cutoff alone, used in some models, does not lead to a k T cutoff.

The symmetric Lund splitting function reinforces the p T cutoff by the factor exp[-b(m 2 h + p 2 T )/Z].

k = canonical quark momentum = mechanical momentum + string momentum flow through OQ.


Pedro Chahuara
email: pedro.chahuara@imag.fr

François Portet
email: francois.portet@imag.fr

Michel Vacher
email: michel.vacher@imag.fr

Location of an Inhabitant for Domotic Assistance Through Fusion of Audio and Non-Visual Data

In this paper, a new method to locate a person using multimodal non-visual sensors and microphones in a pervasive environment is presented. The information extracted from sensors is combined using a two-level dynamic network to obtain the location hypotheses. This method was tested within two smart homes using data from experiments involving about 25 participants. The preliminary results show that an accuracy of 90% can be reached using several uncertain sources. The use of implicit localisation sources, such as speech recognition, mainly used in this project for voice command, can improve performances in many cases.

I. INTRODUCTION

Several technologies have been used to set up smart environments able to ease the person's life and to provide adequate assistance (e.g., infra-red sensors, video cameras, RFID . . . ). Audio processing technology has a great potential to become one of the major human-machine interaction modalities in smart home. It is physically intangible and does not force the user to be at a particular place in order to operate. Moreover, it can provide interaction using natural language so that the user does not have to learn complex computing procedures or jargon. Voice interfaces can be much more suited to disabled people and seniors who have difficulties in moving or seeing, than tactile interfaces (e.g., remote control) which require physical and visual interaction. Moreover, audio processing is particularly adapted to distress situations. A person, who cannot move after a fall but being conscious, can still call for assistance while a remote control may be unreachable. Despite this, few smart home projects have seriously considered audio technology in their design [START_REF] Hamill | Development of an automated speech recognition interface for personal emergency response systems[END_REF], [START_REF] López-Cózar | Multimodal dialogue for ambient intelligence and smart environments[END_REF], [START_REF] Moncrieff | Dynamic privacy in a smart house environment[END_REF]. Part of this can be attributed to the fact that this technology, though mature, still needs to address important challenges [START_REF] Vacher | Development of audio sensing technology for ambient assisted living: Applications and challenges[END_REF].

To improve autonomy, comfort and security at home, we are developing a new smart home system called Sweet-Home whose main human-machine interaction modality is based on audio processing technology. Among the first data processing tasks a smart home must implement, automatic locating of the person is essential. Indeed, the location of the person plays a crucial role to make appropriate decisions for many applications (e.g., home automation orders, heating and light control, dialogue systems, robot assistants) and particularly for health and security oriented ones (e.g., distress call, fall, activity monitoring). Automatic locating becomes particularly challenging when privacy issues prevent the systematic use of video cameras and worn sensors. Another source of localization can be derived from household appliances and surveillance equipment [START_REF] Bellego | A model for the measurement of patient activity in a hospital suite[END_REF], [START_REF] Wren | Toward scalable activity recognition for sensor networks[END_REF]. The analysis of the audio channel is another interesting modality in home automation, which, in addition to providing a voice command, can bring various audio information such as broken glass, slamming doors, etc. [START_REF] Vacher | Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living[END_REF]. There is an emerging trend to use such modality in pervasive environments [START_REF] Bian | Using sound source localization in a home environment[END_REF], [START_REF] Moncrieff | Dynamic privacy in a smart house environment[END_REF], [START_REF] Vacher | Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living[END_REF]. The audio information requires far less bandwidth than video information and can easily detect some activities (e.g., conversations, telephone ringing). However, if the video is sensitive to changes in brightness, the audio channel is sensitive to environmental noise [START_REF] Vacher | Development of audio sensing technology for ambient assisted living: Applications and challenges[END_REF]. It appears that no source taken alone makes possible a robust and cheap location. It is therefore important to establish a location method that would benefit from the redundancies and complementarities of the selected sources.

In this paper, we present a new method developed for automatic dweller location from non-visual sensors. After an introduction to the Sweet-Home project in Section II, the approach we adopted to locate a person is presented in Section III. This approach was tested within real smart homes and the results of the experiments are described in Section IV. The paper ends with a brief discussion of the results.

II. SWEET-HOME: AN AUDIO-BASED SMART HOME SYSTEM

The Sweet-Home project (http://sweet-home.imag.fr) is a French national supported research project aiming at designing a new smart home system based on audio technology focusing on three main aspects: to provide assistance via natural human-machine interaction (voice and tactile command), to ease social inclusion and to provide security reassurance by detecting situations of distress. If these aims are achieved, then the person will be able to pilot their environment at any time in the most natural way possible. To assess the acceptance and fear of this new technology, a qualitative user evaluation was performed. 8 healthy persons between 71 and 88 years old, 7 relatives (child, grand-child or friend) and 3 professional carers have been recruited. During about 45 minutes, they were questioned in co-discovery in a fully equipped smart home alternating between interview and wizard of Oz periods followed by a debriefing. The four important aspects of the project have been assessed: voice command, communication with the outside world, domotic system interrupting a person's activity, and electronic agenda. Succinctly, in each case the voice based solution was far better accepted than more intrusive solutions. Thus, audio technology appears to have a great potential to ease daily living for elderly and frail persons.

The input of the Sweet-Home system is composed of the information from the domotic system transmitted via a local network and information from the microphones transmitted through radio frequency channels. The Sweet-Home system will be piloted by an intelligent controller which will capture all streams of data, interpret them and execute the required actions. The diagram of this intelligent controller is depicted in Figure 1a. The knowledge of the controller is defined using two semantic layers: the low-level and the high-level ontologies. The former ontology is devoted to the representation of raw data and network information description. State, location, value and URI of switches and actuators are examples of element to be managed by the I/O processors. The high level ontology, whose taxonomy is shown in Figure 1b, represents concepts being used at the reasoning level. These concepts are organized in 3 main branches: the Abstract Entity represents the different actions that can be performed in a home and the context in which a home can be (e.g., making coffee, being late), the Physical Entity represents things that are present in the home (e.g., the dweller, electrical devices), and finally, the Event concept represent the transient observations of one abstract entity involving zero or several physical entity (e.g., at 12:03 the dweller is sleeping). This separation between low and high levels makes possible a higher re-usability of the reasoning layer when the sensor network and the home have to be adapted [START_REF] Klein | Ontology-centred design of an ambient middleware for assisted living: The case of soprano[END_REF]. The estimation of the current context is carried out through the collaboration of several processors, each one being specialized in a certain context aspect, such as location detection or activity recognition. All processors share the knowledge specified in both ontologies and use the same repository of facts. Furthermore, the access to the knowledge base is executed under a service oriented approach that allows any processor being registered to be notified only about particular events and to make inferred information available to other processors. This data and knowledge centred approach ensures that all the processors are using the same data structure and that the meaning of each piece of information is clearly defined among all of them. Once the current context has been determined, the controller evaluates if an action must be taken, such as making an emergency call in case of a circumstance of distress.

III. LOCATION OF AN INHABITANT BY DYNAMIC NETWORKS AND SPREADING ACTIVATION

The method developed for locating a person from multiple sources is based on the modelling of the links between events and location assumptions by a two-level dynamic network. Recently, Niessen et al. [START_REF] Niessen | Disambiguating sounds through context[END_REF] presented an approach based on a two-level dynamic networks to disambiguate the recognition of sound events. The input level is composed of sound events, level one represents the assumptions related to an event (e.g., ball bounce or hand clap), and level two is the context of the event (e.g., basketball game, concert, play). Each event activates assumptions according to the input event and the contexts to which these assumptions are linked.

We adapted this method to multisource fusion with the aim of locating a person. The dynamic network that we designed is organized into two levels: the first level corresponds to location hypotheses generated from an event; and the second level represents the contexts for which the activation indicates the most likely location given the previous events. Thus, the activation A h i of the i th hypothesis depends exclusively on the probability given by the event e at time t n which generated it. It is computed using formula 1.

A h i (t n ) = P (location = i | e tn ) (1)
And the activation A c i of the i th context depends on its previous value and its associated hypothesis. It is computed using formula 2.

A c i (t n ) = e -∆t τ A c i (t n -∆t) + A h i (t n ) (2)
time e(tn-1) e(tn) e(tn+1)

CA CB CC CD h 1 (t n-1 ) h 2 (t n-1 ) h 3 (t n-1 ) h 1 (tn) h 2 (tn) h 1 (t n+1 ) h 2 (t n+1 ) h 3 (t n+1 )
Input events Location contexts Hypotheses If there was no prior event, context C B will be the most certain. At time t n , the previous weights will be weighted by e -tn-t n-1 τ to which will be added the weights generated by the assumptions related to the event e(t n ): h 1 (t n ) and h 2 (t n ) towards contexts C B and C D respectively. The introduction of the time constant permits us to estimate the certainty of finding a person in a room according to its latest location. The decrease of this certainty is implemented by the forgetting function e -∆t τ . The method will be applied in the same way at time t n+1 when the context C D will receive the greatest activation and will be selected.

IV. EXPERIMENTATION

A. Pervasive Environments and Data Used

The approach was tested on corpra acquired in two fully equipped smart homes: the Sweet-Home corpus and the HIS corpus. The HIS corpus was acquired during experiments [START_REF] Fleury | A multimodal corpus recorded in a health smart home[END_REF] aiming at assessing the automatic recognition of Activities of Daily Living (ADL) of a person at home in order to automatically detect loss of autonomy. The data considered in this study consisted of about 14 hours of 15 people recordings using the following sensors: 7 microphones (Mic) set in the ceiling; 3 contact sensors on the furniture doors (DC) (cupboards in the kitchen, fridge and dresser in the bedroom); and 6 Presence Infrared Detectors (PID) set on the walls at about 2 metres in height. The Sweet-Home corpus was acquired in realistic conditions, using the DOMUS smart home described in Figure 3. This smart home was designed and set up by the Multicom team of the Laboratory of Informatics of Grenoble to observe users' activities interacting with the ambient intelligence of the environment. The data considered in this study consisted of about 12 hours of 10 people recordings performing daily activities using the following sensors: 7 Mics; 3 DCs on the furniture doors; 4 contact sensors on the indoor doors (IDC); 4 DCs on the windows; and 2 PIDs set on the ceiling.

B. Weight Estimation

The hypothesis-context relationship is, in our case a one-toone relation because a location hypothesis is related to only one room. It is a experimental choice as some assumptions on weakly separated rooms (e.g., lounge/bedroom) could activate several contexts. The weight of this relationship is always 1.

The event-hypothesis relationship is unidirectional and oneto-many. The weights and the hypotheses generated vary according to the source of the event and to the topology of the home. In the case of contact sensors on the furniture and on the windows as well as in the case of the PID, the event was linked to only one hypothesis. Indeed, the DCs and PID delivered non-ambiguous information, thus an event activated with a weight of 1 the hypothesis of the room in which it was situated. For the case of the IDCs of the Sweet-Home corpus, two use cases were considered : the door opening, and the door closing based on the hypothesis that when a door is being open it is more probable that it implies a change of room while when a door is closed the change of room is not certain. Thus, conditional probabilities were computed using the data of 5 participants which were not used in the final test data. When a new DC event arrives at time t n , the weight W for room Room is given by W (Room) = P (Room | DC, S, Context) , where DC, Room ∈ {Study, Bedroom, Kitchen, Bathroom}, the state S ∈ {Open/Close} and the context Context = arg max Room P (Room) at t n-1 . These values were calculated for all the combinations of the variables DC,S and Context. Then, during the execution of the method, weight estimation was performed dynamically using the learned data. Results of the conditional probabilities estimation indicate that most of the time (97% of cases) when door is open from a room then a transition to the contiguous room is produced, whereas when the door is closed the transition is less certain (66% of cases). It seems to support the hypothesis of the two use cases mentioned above. For sound events, the hypothesis weights were computed dynamically. A sound event is generally part of a set of events E = {e j } detected simultaneously on multiple microphones. In this data set, only the sounds classified as speech were considered. Hypotheses were generated from the rooms where the sound events occurred. The weights of the relations event-hypothesis were computed for each location hypothesis p i using the Signalto-Noise Ratio (SNR) of each detection with P (p i | E) = ej ∈E∧ej .room=pi snr l (e j )/ ej ∈E snr l (e j ) where e j is an atomic sound event, p i is the i e room and e j .room (resp e j .snr) is the room where the event was observed (resp. SNR). Thus, the sound event with the highest SNR generates the most likely hypothesis. For each participant's record, the events from DC, PID and Mic were used to activate a dynamic network to estimate the location of the inhabitant. Location performance was evaluated every second by comparing the context of the highest weight to the ground truth. If they matched, then it was a true positive (TP), otherwise it was a confusion. The results were summarised in a confusion matrix from which the accuracy Acc was computed by Acc = nb(T P ) nb(test) where nb(test) corresponds to the duration of the record in seconds. Table I shows the results of both corpus.

C. Results

Sensor

For Sweet-Home it is clear that the fusion of information improved the accuracy since it rises as more sensors information are combined. Even when the precision of infrared sensors was good, the overall results of the method using only these sensors is low (63%) as only two of them have been set in the 4-room flat. This led to a poor sensitivity. In the second row, the accuracy without using the information of door contact on room doors is reported. It can be noticed that the learned probabilities have a significant impact on the performance. In every case, DC on room doors had a positive impact on the performances. From the results on HIS corpus, it can be noticed that, in some cases, fusion of information did not improve the accuracy. The door contact information slightly improved the accuracy compared to that obtained only with the infrared sensors. On the other hand, adding the sound information decreased the performance (91 % versus 96 %). One reason for this may the high level of confusion between sound and speech of the AUDITHIS system [START_REF] Vacher | Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living[END_REF] which reached 25% of classification errors.

Nevertheless, the sound information was useful to improve localisation in some cases. Indeed, there were situations where the room change was not detected by the PID while speech was well identified, which compensated for the low infrared sensitivity. The last line of Table I shows the results for participant #10. In this case, it is clear that each source taken alone did not lead to good location but that the combination of sources provided a clear performance gain (60 % to 97 %). Figure 4 shows the evolution of the value of the activation for each of the event occurrences and the decisions made. The results showed that the information fusion by spreading activation is of interest even when the sources have very good accuracy. It is the case for infrared sensors (but with imperfect sensitivity) and for door contact sensors. The use of less certain localisation sources, such as speech recognition, can then improve performance in many cases. Another important finding is that it is possible to leverage the semantics of the events to gain a higher accuracy as was done with the contact sensors of the room doors for the Sweet-Home corpus. In that case, the knowledge of room transitions was expressed in terms of conditional probabilities and its exploitation was demonstrated to be useful.

The next step is to continue the implementation of the intelligent controller, to implement the voice command recognition, and to test the general suitability of the approach by confronting the system to actual users (elderly and frail people). More challenging tasks will be to make speech recognition robust to environmental noise and to be able to deal with several users.

  (a) The Intelligent Controller Diagram (b) Excerpt of the high level ontology

Fig. 1 :

 1 Fig. 1: Intelligent Controller diagramm and excerpt of the Sweet-Home ontology.

Fig. 2 :

 2 Fig. 2: Example of a Dynamic network

Fig. 3 :

 3 Fig. 3: Layout of the DOMUS smart home and position of the sensors.

Fig. 4 :

 4 Fig. 4: Example showing changes in levels of activation contexts and selected contexts for the possible locations

TABLE I :

 I Accuracy with several combinations of sources

ACKNOWLEDGMENT

The authors would like to thank the participants who performed the experiments. This work is supported by the Agence Nationale de la Recherche (ANR-09-VERS-011).


Benjamin Lecouteux
email: benjamin.lecouteux@imag.fr

Michel Vacher
email: michel.vacher@imag.fr

François Portet
email: francois.portet@imag.fr

Distant Speech Recognition for Home Automation: Preliminary Experimental Results in a Smart Home

Keywords: component, distant speech recognition, keyword detection, triggered language models, home automation, smart home I

This paper presents a study that is part of the Sweet-Home project which aims at developing a new home automation system based on voice command. The study focused on two tasks: distant speech recognition and sentence spotting (e.g., recognition of domotic orders). Regarding the first task, different combinations of ASR systems, language and acoustic models were tested. Fusion of ASR outputs by consensus and with a triggered language model (using a priori knowledge) were investigated. For the sentence spotting task, an algorithm based on distance evaluation between the current ASR hypotheses and the predefine set of keyword patterns was introduced in order to retrieve the correct sentences in spite of the ASR errors.

The techniques were assessed on real daily living data collected in a 4-room smart home that was fully equipped with standard tactile commands and with 7 wireless microphones set in the ceiling.

Thanks to Driven Decoding Algorithm techniques, a classical ASR system reached 7.9% WER against 35% WER in standard configuration and 15% with MLLR adaptation only. The best keyword pattern classification result obtained in distant speech conditions was 7.5% CER.

INTRODUCTION

The evolution of ICT led to the emergence of smart homes equipped with ambient intelligence technology which provides high man-machine interaction capacity. Given the increase of life expectancy, these smart homes represent a promising solution to enable the elderly and frail persons to live in their own home as autonomously as possible. However, this calls for technological solutions that suit their specific needs and capabilities. Classical tactile commands may not be adapted to this population and can be complemented by speech based solutions that would provide voice command and easy interactions with their relatives or with professional carers in case of distress situations (e.g., a person who cannot move after a fall). Moreover, analysis of sounds emitted in a person's habitation may be useful for activity monitoring.

To make natural interaction with home automation possible at any time and from anywhere in the house, the Sweet-Home project was set up to integrate sound based technology within smart homes. As emphasized by Vacher et al. [START_REF] Vacher | Development of Audio Sensing Technology for ambient Assisted Living: Applications and Challenges[END_REF], major challenges still need to be overcome in these environments including robust distant speech recognition in noisy uncontrolled situations and correct identification of domotic orders in continuous audio recording conditions (i.e., the user does not press a button when she speaks to the "house"). This paper presents preliminary results of speech recognition techniques evaluated on realistic data. Before presenting how these data were acquired (Section 3), some background about the Sweet-Home project and the challenges to tackle is given in Section 2. The study consisted in two tasks. In Section 4, the first task consisting in evaluating several techniques for multi-source speech recognition is detailed. The second task, presented in Section 5, was devoted to word spotting in the recognized sentences. The paper concludes with brief remarks about the results and future work.

II. BACKGROUND

A. SWEET-HOME

The Sweet-Home project is a French national supported research project 1 . The project team is made up of researchers and engineers from the Laboratory of Informatics of Grenoble (specialized in speech processing, smart home design and evaluation), the Esigetel (specialised in audio technology) and from three companies: Theoris (real-time system development and integration), Camera Contact (diffusion and integration of adapted services for maintenance at home) and Technosens (remote health-care equipment for the elderly). The project 2 aims at designing a new smart home system by focusing on three main aspects: to provide assistance via natural man-machine interaction (voice and tactile command), to ease social inclusion and to provide security reassurance by detecting situations of distress. If these aims are achieved, then the person will be able to pilot her environment at any time in a most natural way.

The Sweet-Home system is depicted in Figure 1. The input of the system is composed of the information from the domotic system transmitted via a local network and 1 information from the microphones transmitted through radio frequency channels. While the domotic system provides symbolic information, raw audio signals must be processed to extract information from speech and sound. This extraction is based on our experience in developing the AuditHIS system [START_REF] Vacher | Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living[END_REF], a real time multi-threaded audio processing system for ubiquitous environments. The extracted information is analyzed and either the system reacts to an order given by the user or the system acts proactively by modifying the environment without an order (e.g. turns off the light when nobody is in the room). Outputs of the system thus include domotic orders, but also interaction with the user in the case when a vocal order was not understood for example, or in case of alert messages (e.g. turns off the gas, remind the person of an appointment). The system also has the possibility to make it easier for the user to connect with their relative, physician or caregiver by using the e-lio 3 or Visage4 systems. In order for the user to be in full control of the system and also in order to adapt to the users' preferences, three ways of commanding the system are possible: voice order, PDA or classical tactile interface (e.g. switch).

The project does not include the definition of new communication protocols between devices. Rather than building communication buses and purpose designed material from scratch, the project tries to make use of already standardized technologies and applications. As emphasized in [START_REF] Mäyrä | Probing a proactive home: Challenges in researching and designing everyday smart environments[END_REF] (whose authors used X10 for their home automation bus), standards ensure compatibility between devices and ease the maintenance as well as orient the smart home design toward cheaper solutions. The interoperability of ubiquitous computing elements is a well known challenge to address [START_REF] Edwards | At home with ubiquitous computing: Seven challenges[END_REF]. Another example of this approach is that Sweet-Home includes systems which are already specialized to handle the social inclusion part. We believe this strategy is the most realistic one given the large spectrum of skills that are required to build a complete smart home system.

B. Automatic Speech Recognition in smart homes

Automatic Speech Recognition systems (ASR) have reached good performances with close talking microphones (e.g. head-set), but the performances decrease significantly as soon as the microphone is moved away from the mouth of the speaker (e.g., when the microphone is set in the ceiling). This deterioration is due to a broad variety of effects including reverberation and presence of undetermined background noise such as TV, radio and devices. All these problems should be taken into account in the home context.

1) Echo and reverberation

Adaptation of ASR to distorted signals may be done either at the acoustic model level or at the input (feature) level [START_REF] Wölfel | Distant Speech Recognition[END_REF]. Deng et al [START_REF] Deng | Large-vocabulary speech recognition under adverse acoustic environments[END_REF] demonstrated that adapted feature domain techniques provide better performances than those obtained by systems trained with data undergoing the same distortion as the actual data (e.g., model learned with distorted data) for both stationary and non stationary noise conditions. Moreover, for reverberation time above 500 ms, the ASR performance is not significantly improved when the acoustic models are trained on data recorded in the same reverberation condition [START_REF] Baba | Speech recognition by reverberation adapted acoustic model[END_REF]. In the home involved in the study, the only glazed areas that are not on the same wall are right-angled, thus the reverberation is minimal. Given this and the small room dimensions we can assume that the reverberation time stays below 500 ms.

2) Background noise

When the noise source perturbing the signal of interest is known, various techniques can be employed [START_REF] Michaut | Filtrage adaptatif : théorie et algorithmes[END_REF]. One technique is to assign a microphone to record the noise source and estimate the impulse response of the room acoustic to remove the noise [START_REF] Valin | On adjusting the learning rate in frequency domain echo cancellation with double talk[END_REF]. This impulse response can be estimated through Least Mean Square (LMS) or Recursive Least Square (RLS) methods. In a smart home, these methods showed acceptable results when the noise is composed of speech or classical music [START_REF] Vacher | Speech recognition in a smart home: some experiments for telemonitoring[END_REF]. In case of unknown noise sources, such as vacuum or ventilator, Blind Source Separation (BSS) techniques may be used. The audio signal captured by the microphone is composed of mixture of speech and noise sources. Independent Component Analysis (ICA) is a subcategory of BSS which attempts to separate the different sources through their statistical properties (i.e., purely data driven). This method is especially efficient for non-Gaussian signals (such as speech) and does not need to take into account the position of the sources or of the microphones.

3) Sentence spotting

Term detection has been extensively studied in the last decades in the two different contexts of spoken term detection: large speech databases and keyword spotting in Experiences undertaken in user's home conditions showed a decrease in performance. In [START_REF] Reidel | Pilot study of an interactive voice response system to improve medication refill compliance[END_REF], an IVR was set up to help elderly people with their medication. Over the 300 persons recruited, a third stopped the experiment because they complained about the system and only 38 persons completed the experiment. In more realistic conditions, such as noisy or spontaneous speech, performances are dramatically degraded by recognition errors [START_REF] Vacher | Preliminary Evaluation of Speech/Sound Recognition for Telemedicine Application in a Real Environment[END_REF]. In this study, only homes, for which reverberation can be neglected, are considered. Therefore, only classical ASR techniques with adaptation using data recorded in the test environment are considered. For the application, some aspects of both spotting and large vocabulary continuous speech recognition (LVCSR) are encountered. Our approach is based on the use of LVCSR systems in order to increase the recognition robustness. We propose investigation in language and acoustic models adaptation and multi-source based recognition. Finally, to improve the detection rate, we propose an original approach which integrates domotic order matching directly inside the ASR system.

III. EXPERIMENTAL FRAMEWORK

To test the approach, one experiment was conducted to acquire speech corpora composed of utterances of domotic orders, distress calls and casual sentences. This corpus, called the SWEET-HOME speech corpus, was acquired in a real smart home using several microphones set in the ceiling. This corpus was used to tune and to test two classical ASR systems in different configurations. This section briefly introduces the corpus and the ASR systems.

A. Data acquisition in the DOMUS smart home

The SWEET-HOME speech corpus was acquired in realistic conditions, i.e., in a smart-home and in distant speech condition. To do so, the DOMUS smart home was used. This smart home was designed and set up by the Multicom team of the Laboratory of Informatics of Grenoble to observe users' activities interacting with the ambient intelligence of the environment. Figure 2 shows the details of the flat. It is a thirty square meters suite flat including a bathroom, a kitchen, a bedroom and a study, all equipped with sensors and effectors so that it is possible to act on the sensory ambiance, depending on the context and the user's habits. The flat is fully usable and can accommodate a dweller for several days. The technical architecture of DOMUS is based on the KNX bus system (KoNneX) 5 , a worldwide ISO standard (ISO/IEC 14543) for home and building control. More than 150 sensors, actuators and information providers are managed in the flat. For the need of the SWEET-HOME project, the flat has also been equipped with 7 radio microphones set into the ceiling (2 per room except for the bathroom) that can be recorded in real-time thanks to a 5 www.knx.org dedicated PC embedding an 8-channel input audio card [START_REF] Vacher | Development of Audio Sensing Technology for ambient Assisted Living: Applications and Challenges[END_REF].

A full record of the sound flow of the 7 channels was done for each speaker during the entire experiment. The first phase consisted in following a scenario of activities without condition on the time spent and the manner of achieving them (having a breakfast, simulate a shower, get some sleep, clean up the flat using the vacuum, etc.). A visit, before the experiment, was organized to make sure that the participants will find all the items necessary to perform the activities. During this first phase, participants uttered forty predefined casual sentences on the phone (e.g., "Allo", "J'ai eu du mal à dormir") but were also free to utter any sentence they wanted (some did speak to themselves aloud). Note that only audio information from the seven microphones on the ceiling was captured and not from the microphone of the telephone. Data from video cameras and the domotic network were also captured but their descriptions are out of the scope of this paper.

The second phase consisted in reading aloud a list of 44 sentences whose 9 were distress sentences and 3 were domotic orders. This list was read in 3 rooms (study, bedroom, and kitchen) under three conditions: with the vacuum on, with the radio on (vacuum off) and without noise (everything off). So each participant read the text nine times in total, which makes 396 sentences. Only the clean condition will be used in this paper, the noisy condition records having been designed for other experiments.

B. SWEET-HOME speech corpus

For the phase 1, only the sentences uttered in the study during the phone conversation were considered. For the phase 2, only the sentences uttered in the kitchen without additional noise (vacuum or radio) were considered. This corpus was indexed manually because each speaker did not follow strictly the instructions given at the beginning of the experiment. Some hesitations and word repetitions occurred along the records. Moreover, when two sentences were uttered without a sufficient silence between them, some of these couples were considered as one sentence. A summary of the corpus is given in Table 1. The SWEET-HOME speech corpus is made of 862 French sentences uttered by 21 persons in the first phase, 917 French sentences in the second phase; it lasts for each channel 38 minutes 46s in the case of the first phase, and 40 minutes 27 s in the case of the second phase. The average SNR (Signal-to-Noise Ratio) for the considered sentences is 20.3 dB.

C. ASR systems

Two classical ASR systems were used in the study: Sphinx [START_REF] Seymore | The 1997 cmu sphinx-3 english broadcast news transcription system[END_REF] and Speeral [START_REF] Nocera | Principes et performances du décodeur parole continue speeral[END_REF]. This section introduces both system and the French language models used. We used previously computed French acoustic models.

1) The Speeral ASR system The LIA (Laboratoire d'Informatique d'Avignon) speech recognition tool-kit Speeral relies on a decoder with HMMbased context-dependent acoustic models and trigram language models. HMMs are classical three-state left-right models and state tying is achieved by using decision trees. Acoustic vectors are composed of 12 PLP (Perceptual Linear Predictive) coefficients, the energy, and the first and second order derivatives of these 13 parameters. Speeral was used to build a system dedicated to broadcast news; it was involved in the ESTER evaluation campaign [START_REF] Nocera | Principes et performances du décodeur parole continue speeral[END_REF]. In the study, the acoustic models were trained on ESTER materials (about 80 hours of annotated speech). Given the targeted application of SWEET-HOME the computation time should not be a breach of real-time use. Thus, the 1xRT Speeral configuration was used. In this case, the time required by the system to decode one hour of speech signal is real-time (noted 1xRT). The 1xRT system uses a strict pruning scheme.

2) The Sphinx ASR system

The CMU Sphinx 3.3 (fast) decoder [START_REF] Seymore | The 1997 cmu sphinx-3 english broadcast news transcription system[END_REF] is a branch of the CMU Sphinx III project which has been developed to increase the speed of the algorithm. This decoder uses fully continuous acoustic models with 3 or 5-state left-to-right HMM topologies. Acoustic vectors are composed of 13 MFCC coefficients, the delta and the double delta of each coefficient. HMM-based context-dependent acoustic models were trained on the BREF 120 corpus [START_REF] Gauvain | Design considerations and text selection for BREF, a large french read-speech corpus[END_REF] which is composed of about 100 hours of annotated speech from 120 French speakers. We also attempted to use ESTER data for the training; however results were much degraded and are not presented in this paper.

3) Language models For each ASR, the acoustic model was different but the same 3-gram language model with a 10K lexicon was used with both. Two language models were considered in the study: the generic and the specialized models. The generic language model was estimated on about 1000M of words from the French newspapers Le Monde and Gigaword, and the broadcast news manual transcripts provided during the ESTER campaign. The specialized language model was estimated from the sentences that the 21 participants had to utter during the experiment (domotic orders, casual phrases, etc.).

IV. SPEECH RECOGNITION TASK

The two ASR systems (Sphinx and Speeral) performances were compared in 1xRT conditions. In order to propose a baseline system, the adaptation of both acoustic and language models was tested respectively on Sphinx and Speeral. Then, to improve the robustness of the recognition, multi-source ASR was tested. Finally, a new variant of a driven decoding algorithm was used in order to take into account available a priori information and several audio channels for each speaker.

In each case, the phase 1 of the corpus was used for development and MLLR speaker adaptation while the phase 2 was used for performances estimation. Results obtained on the phase 2 of the corpus were compared at two levels: the Word Error Rate (WER) and the Classification Error Rate (CER). The WER is a good measure for the robustness, while the CER corresponds to the main goal of our research (i.e., detection of predefined sentences).

A. Acoustic models adaptation: MAP versus MLLR

To improve the recognition, acoustic models were adapted for each speaker by using two methods: Maximum A Posteriori (MAP) and Maximum Likelihood Linear Regression (MLLR). This was achieved by using data of the first phase. These data were perfectly annotated, allowing performing correct targeted speaker adaptation.

The Maximum Likelihood Linear Regression (MLLR) is used when a limited amount of data per class is available. MLLR is an adaptation technique that uses small amounts of data to train a linear transform which warps the Gaussian means so as to maximize the likelihood of the data. The principle is that acoustically close classes are grouped and transformed together. In the case of the Maximum a posteriori approach (MAP), initial models are used as informative priors for the adaptation.

The figure 3 shows results for the ASR systems without adapted acoustic models. All experiments are carried out with the generic language model (GLM) lightly interpolated with predefined sentences (PS) presented in the next section (GLM: 90%, PS: 10%). Without acoustic adaptation, the mean WER is 57.7% for Sphinx and 35% for Speeral.

The figure 4 presents the results using MAP adaptation. They show that MAP is not very relevant in this particular case. With MAP, the Speeral WER is 28.5% and the Sphinx WER is 62%. Two aspects explain this:

•

The lack of parameter tying in the standard MAP algorithm implies that the adaptation is not robust.

•

The noisy environment is not suited to MAP adaptation [START_REF] Wang | A new approach for incremental speaker adaptation[END_REF].

The best results are obtained with MLLR adaptation, which is the best choice for sparse and noisy adaptation data whatever the channel. The results are presented in figure 5. The mean Speeral WER is 14.5% while the Sphinx WER is 27.3%.

B. Reducing linguistic variability

In this section, we propose some linear interpolation schemes where specific weights are tested on specialized and generic language models. The reduction of the linguistic variability thanks to the contribution of known predefined sentences is explored. Better recognition can be obtained by reducing the overall linguistic space. This can be achieved by estimating a language model on the expected domotic orders. However, such a language model would be probably too specific when the speaker deviates from the original transcript.

Therefore, we interpolated a specialized model with a generic large vocabulary language model. Two schemes of linear interpolation were considered: in the first one, the generic model had a strong weight; in the second one, the impact of the generic model was low. The results of ASR in four conditions -generic language model only, specialized model only, and the two interpolationsare presented below. The ASRs were assessed after MLLR adaptation using the data of phase 1 of the corpus.

The figure 6 presents ASR systems WER with the generic language model (Baseline). As expected, the baseline language model obtained poor results: about 85.2% of WER for Sphinx and 75.3% for Speeral. Without reliable information, the ASR systems, in noisy, speaker independent and large vocabulary condition were unable to perform good recognition.

The figure 7 presents ASR systems WER with the specialized language model. The systems were able to detect more predefined sentences. However when the speaker deviates from the scenario, the language model is unable to find the correct uttered sentence. The language model is thus too specific.

Finally, a light (10%) interpolated language model led to the best results, which are presented in Figure 8. This model combined the generic language model (with a 10% weight) and the specialized model (with 90% weight). These results show that a decoding based on a language model mainly learnt from the predefined sentences improves significantly the WER. However, the best WER is obtained when a generic language model is also considered: when the speaker deviates, the generic language model allows to correctly recognize the pronounced sentences.

C. Conclusion about monosource ASR with Sphinx and Speeral

Sphinx and Speeral were assessed taking into account realistic distant-speech conditions and a home automation application (voice command). Thus, each of the systems had to perform ASR with several constraints and opportunities. Indeed, the noisy, distant-speech, multispeaker (more than one person may be in the home), continuous analysis and real-time aspects put the experiment in more difficult conditions than the classical head-set one. In the experiment, the two ASR systems did not give the same performances. In this special case, we observe that Speeral led to the best results. However these differences must be further explored, because acoustic models were not trained on the same data set.

In our next experiments, the selected baseline system is based on Speeral with a specific MLLR adaptation for each speaker and an interpolated language model. The application conditions also make the ASR systems benefit from multiple audio channels, from a reduced vocabulary and from the hypothesis that only one speaker should utter voice commands. On the two systems, lightly interpolated language model and a MLLR acoustic adaptation did improve significantly the ASR system performance, with Speeral still leading to the lowest WER. In the next section, we propose several techniques to perform multi-source ASR system and to combine systems.

D. ROVER

The ROVER algorithm [START_REF] Fiscus | A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER)[END_REF] (Recognizer Output Voting Error Reduction) allows voting methods to be used for wordlevel system combination within large vocabulary speech recognition tasks. This scheme makes use of the one-best hypothesis from a set of speech recognizers, with optional confidence associated for each output. The ROVER vote is an easy way to combine multiple systems. In this study, ROVER was used to benefit from a potential complementarity of the ASR systems. In our case, confidence scores were not associated to system outputs: a majority vote was used.

Results of the ROVER applied to the experimental data in multiple ASR systems configurations are presented in figure 9. We combined all systems:

• Speeral MLLR streams 1 & 2 • Sphinx MLLR streams 1 & 2
Stream 1 is related to channel 4 and stream 2 to channel 5. Despite of the poor Sphinx performance, the ROVER combination led to great improvements. The results show that the ROVER makes possible robust ASR with a mean WER of 10.2%: this aspect show the complementarity of the systems and the streams. However, the ROVER stage increased the computation time by the number of combined systems. Given that the objective of the project is to build a real-time and affordable solution, computational resources are limited. Moreover, ROVER combination for two systems reduces the problem to picking the word with the highest confidence when two systems disagree. Thus, when the recognizer confidence scores are not reliable, the ROVER between two systems does not perform well and the final performance is likely to be similar to a single system. Thus, we propose in the next section a method allowing low-cost computations with only two streams, based on the Driven Decoding Algorithm. Then ROVER results are used as baseline in next experiments.

E. Driven Decoding Algorithm

We recently proposed the Driven Decoding Algorithm (DDA) [START_REF] Lecouteux | Text island spotting in large speech databases[END_REF][START_REF] Lecouteux | Generalized driven decoding for speech recognition system combination[END_REF] which is able to simultaneously align and correct imperfect ASR outputs [START_REF] Lecouteux | Imperfect transcript driven speech recognition[END_REF]. DDA has been implemented within SPEERAL: The ASR generates assumptions as it walks the phoneme lattice. For each new step, the current assumption is aligned with the approximated hypothesis. Then a matching score α is computed and integrated with the language model: In the DOMUS smart home, uttered sentences were recorded using two microphones per room. Thus two microphones can be used as input to DDA in order to increase the robustness of the ASR systems as presented in Figure 10. We propose to use a variant of the DDA where the output of the first microphone is used to drive the output of the second one. This approach presents two main benefits:

•

The second ASR system speed is boosted by the approximated transcript (only 0.1xRT)

•

While a ROVER does not allow combining efficiently two systems without confidence scores, DDA combines easily the information.

The Figure 10 explains the Driven Decoding solution: the first Speeral pass on the stream 1 is used to drive a second pass on the stream 2, allowing combining the information of the two streams.

Results using the two stream-DDA are presented in Figure 11. In most cases, DDA generated hypothesis that led either to the mean WER of the two initial streams or to better WER. These results underline an interesting feature of the DDA in comparison with a simple ROVER to combine two systems: the mean WER is 12.3%. We propose to extend this approach in the next section by driving the ASR system by a priori sentences selected on the first stream.

F. Two level DDA

In the previous approach, the first stream of decoding was used to drive the second one: DDA aims to refine the decoding achieved during the first stream decoding. Word spotting using ASR systems is known to be focused on accuracy, since the prior probability of having the targeted terms in a transcription is low. On the other hand, transcription errors may introduce mistakes and lead to misses of correct utterances, especially on large requests: the longer the searched term, the higher the probability of encountering an erroneous word. In order to limit this risk, we introduced a two-level DDA [START_REF] Lecouteux | Distant Speech Recognition in a Smart Home: Comparison of Several Multisource ASRs in Realistic Conditions[END_REF]: speech segments of the first pass are projected in 3-best spotted sentences and injected via DDA into the ASR system for the second decoding pass. The first decoding pass allows generating hypotheses. By using the edit distance explained in [START_REF] Lecouteux | Distant Speech Recognition in a Smart Home: Comparison of Several Multisource ASRs in Realistic Conditions[END_REF], closed spotted sentences are selected and used as input for the fast second pass as presented in Figure 12. In this configuration, the first pass is used to select some sentences used to drive the second pass. In the Figure 12, the first system output is " allumer la lumière". The edit distance allowed finding two closed sentences: "allumez la lumière" and "allumez la télévision". These sentences drive the second pass and allow one to find the good output "allumez la lumière".

Results using this approach are showed in Figure 13. According to the WER, this approach improved significantly the ASR system quality, by taking advantage of the a priori information assessed by the predefined spotted sentences. WER is improved significantly for all speakers: the mean WER is 7.9%.

VI. CONCLUSION

This paper describes a study of speech recognition and sentence spotting in a smart home implying noisy and distant speech conditions. Two ASR systems: Sphinx and Speeral working with different graph exploration algorithms were used to build the baseline system. The best baseline system was composed of interpolated language models between a large vocabulary one and a specialized one (composed of predefined domotic sentences); and used MLLR acoustic adaptation. The best baseline obtained a 14.5% WER and a 16.9% CER.

In a second part, an original approach was proposed to benefit from the multiple microphones of the smart home and a priori knowledge about the sentences being uttered. This approach is base on the Driven Decoding Algorithm which permits to drive a stream being decoded by the results of the decoding on another one. This approach was refined by integrating a priori knowledge in the DDA about the sentences to spot. Experimental results showed that these approaches brought significant gain compared to the baseline: the best results were obtained by a two-level DDA. DDA led to both a WER (7.9%) and CER (7.5%) improvement. Moreover, results were better than the ones of a ROVER based system working with more than 5 times real time while the two-level DDA approach worked in about 1xRT. In fact, DDA benefits from the a priori knowledge to speed up the speech recognition by reducing the search space.

This study shows that good recognition rate can be obtained by adapting classical ASR systems mixing multisource and domain knowledge. However, in a smart home many unknown audio sources can perturb the speaker voice. This noise must be removed to permit the system being efficient even in noisy condition. We plan to test these approaches in noisy conditions with Independent Component Analysis (ICA) in order to separate speech from undetermined noise.

Figure 1 .

 1 Figure 1. The SWEET-HOME diagram

Figure 2 .

 2 Figure 2. The Domus Smart HomeFrom October 1 to November 30 2010, 21 persons (including 7 women) participated to a 2-phase experiment to record, among other data, speech corpus in a daily living context. To make sure that the audio data acquired would be as close as possible to real daily living sounds, the participants were asked to perform several daily living activities in the smart home. The average age of the participants was 38.5±13 years (22-63, min-max) and each experimental session lasted about 2 hours. No instruction was given to any participant about how they should speak and in which direction. Consequently, no participant emitted sentences directing their voice to a particular microphone.

Figure 3 .

 3 Figure 3. ASR system recognition WER without acoustic adaptation. From left to right: two Sphinx streams and two Speeral streams

Figure 4 .

 4 Figure 4. ASR system recognition WER with MAP acoustic adaptation. From left to right: two Sphinx streams and two Speeral streams

Figure 5 .

 5 Figure 5. ASR system recognition WER with MLLR acoustic adaptation. From left to right: two Sphinx streams and two Speeral streams

Figure 6 .

 6 Figure 6. ASR system recognition WER with the baseline Language Model. From left to right: two Sphinx streams and two Speeral streams

Figure 7 .

 7 Figure 7. ASR system recognition WER with LM trained on a priori sentences keywords. From left to right: two Sphinx streams and two Speeral streams

Figure 8 .

 8 Figure 8. ASR system recognition WER with the specilized LM. From left to right: two Sphinx streams and two Speeral streams

Figure 9 .

 9 Figure 9. ASR system recognition WER by using ROVER

  probability of the trigram. When the trigram is aligned, α is at a maximum and decreases according to the misalignments of the history (values of α must be determined empirically using a development corpus).

Figure 10 .Figure 11 .

 1011 Figure 10. Driven Decoding Algorithm used with two streams: The first stream allows one to drive the second stream

Figure 12 .

 12 Figure 12. Driven Decoding Algorithm used with two streams and a priori sentences. The first stream allows one to drive the second stream, according to a refine selection of spotted sentences.

  structuring of audio-visual collections. Performances reported in the literature are quite good in clean conditions, especially with broadcast news data.

	continuous speech streams. The first topic recently faced a
	growing interest, stemming from the critical need of
	content-based
				Specialized device:
				Connection with Relatives,
	KNX Network			Physicians or Carers
	Activators		Decision Stage
		Filtering &		Speech	Sound
		Layout		Processing	Processing
	Switches
	Sensors	Domotic Management	Adapting Stage	Sound Quality Stage
			Speaker	Microphones

TABLE I

 I

		.	SWEET-HOME SPEECH CORPUS DESCRIPTION
	Spkr.			Phase 1		Phase 2
	ID	Duration	SNR mean SNR mean	Duration
		(s)		(dB)	(dB)	(s)
		Channel 6 or 7	Channel 6	Canal 7	Channel 4 or 5
	1	145.78	23.54	22.08	96.66
	2	119.36	22.64	21.04	110.42
	3	112.08	14.80	12.21	119.76
	4	141.32	16.51	16.47	119.04
	5	159.32	29.70	26.75	122.21
	6	122.10	17.65	16.11	108.61
	7	110.90	19.04	17.52	116.00
	8	114.54	20.31	18.99	114.64
	9	121.58	26.80	24.73	135.36
	10		77.50	20.27	18.00	104.54
	11	106.52	20.22	21.06	105.76
	12		90.48	24.50	21.08	108.44
	13		96.46	26.24	19.88	116.52
	14		97.74	17.69	17.66	113.40
	15		96.48	22.55	21.36	101.98
	16		96.86	21.39	17.61	106.72
	17	111.08	21.66	20.00	144.46
	18	169.14	19.97	19.04	124.52
	19	146.98	25.12	23.41	125.58
	20		89.80	27.46	24.77	120.60
	21		99.48	19.46	19.18	109.56

http://www.technosens.fr/

http://camera-contact.com/

ACKNOWLEDGMENT

The authors would like to thank S. Méniard who developed the StreamHIS software used for multichannel sound recording. They also are very grateful to the participants who took part to the different experiments. Thanks are also extended to B. Meillon, N. Bonnefond and S. Pons for their support during the experiment.

This work is a part of the Sweet-Home project founded by the French National Research Agency (Agence Nationale de la Recherche / ANR-09-VERS-011) 2 http://sweet-home.imag.fr/

G. Two level DDA: conclusion

In the case of constrained computational resources, the ASR system Speeral seems more accurate than the ASR system Sphinx. Nevertheless, the ROVER between all systems shows that a part of information is lost. Then, we proposed two original approaches based on the Driven Decoding Algorithm. By using the two streams available the ASR system is able to combine them efficiently. The best results are obtained with the two level approach were the ASR system is driven by both the first stream and the potential spotted sentences. The next section investigates the impact of each previous proposed method on the detection of pronounced sentences.

V. DETECTION OF PREDEFINED SENTENCES

In order to spot sentences into automatic transcripts T of m characters, each sentence of n characters from predefined sentences H was aligned T by using a Dynamic Time Warping (DTW) algorithm at the letter level [START_REF] Berndt | Using dynamic time warping to find patterns in time series[END_REF]. Sequences were aligned by constructing an n-by-m matrix where the element of the matrix contained the distance between the two words and using the distance function defined below.

The deletion, insertion and substitution costs were computed empirically. The cumulative distance ) , ( j i γ between j H and i T is computed as:

( )

Each predefined sentence is aligned and associated with an alignment score: the percentage of well aligned symbols (here letters). The sentence with the best score is then selected as best hypothesis.

This approach takes into account some recognition errors such as word declinations or light variations ("téléviseur", "télévision", etc.). Moreover, in a lot of cases, a missdecoded word is orthographically close from the good one (due to the close pronunciation).

To test the detection of a priori pronounced sentences, such as domotic orders (e.g., "allume la lumière" "turn on the light"), the detection methods were applied in the following ASR configurations:

• Baseline: Speeral system with acoustic and language model adaptation.

• ROVER: Consensus vote between all systems.

• DDA1: DDA drived with the first stream.

• DDA2: DDA drived by the first stream and the spotted sentences.

The Figure 14 presents the correct detected predefined sentences (e.g.,

CER -1

) in the 4 configurations. As expected, the three systems based on ROVER and DDA gave the best performances, with respectively 88.2%, 87,4% and 92.5% of correct classifications. It can be observed that the 2-level DDA based ASR system was able to detect more spotted sentences with less computational time and with more accuracy than the ROVER based one.

A. Spotting task: conclusion

In all best-configurations, predefined sentence recognition had a good accuracy: the baseline recognition gave 83.1%. It can be observed that in other configurations the spotting task correlated with the WER. Thereby ROVER and the two DDA configurations led to a significant improvement over the baseline. The best configuration based on the two-level DDA gave 92.5% of correct classifications.