Michel Vacher
email: michel.vacher@imag.fr

Noé Guirand

Jean-François Serignat

Anthony Fleury

Norbert Noury

Speech Recognition in a Smart Home: Some Experiments for Telemonitoring

Keywords: Noise Suppression, Smart Home, Speech Recognition, Telemonitoring (key

Because of the aging of the population, low-cost solutions are required to help people with loss of autonomy staying at home rather than in public health centers. One solution is to assist human operators with smart information systems. In this case, position and physiologic sensors already give important information, but there are few studies about the utility of sound in patient's habitation. However, sound classification and speech recognition may greatly increase the versatility of such a system: this will be provided by detecting short sentences or words that could characterize a distress situation for the patient. Moreover, analysis and classification of sounds emitted in patient's habitation may be useful for patient's activity monitoring. In this paper, we present a global speech and sound recognition system that can be set-up in a flat. Eight microphones were placed in the Health Smart Home of Grenoble (named HIS, a real living flat of 47m2) to automatically analyze and classify different sounds and speech utterances (e.g.: normal or distress French sentences). Sounds are clustered in eight classes but this aspect is not discussed in this paper. For speech signals, an input utterance is recognized and a subsequent process classifies it in normal or distress, by analysing the presence of distress keywords. An experimental protocol was defined and then this system has been evaluated in uncontrolled conditions in which heterogeneous speakers were asked to utter predetermined sentences in the HIS. The results of this experiment, where ten subjects were involved, are presented. The Global Error Rate was 15.6%. Moreover, noise suppression techniques were incorporated in the speech and sound recognition system in order to suppress the noise emitted by known sources like TV or radio. An experimental protocol was defined and tested by four speakers in real conditions inside a room. Finally, we discuss the results of this experiment as a function of the noise source: speech or music.

INTRODUCTION

The constant growing of life expectancy in the world yields a lack of places and workers in institutions able to take care of elderly people. Researcher teams all over the world try to tackle this issue by working on ways to maintain elderly people in their own home as long as possible. Geriatrics is thus in great need for systems assessing the evolution of a person in her environment and detecting the appropriate moment for admitting that person in an institution. Smart homes can be classified according to the types of equipment and systems installed. The residents of smart homes are not just those with severe pathologies or chronic illness, but also those who want a better quality of life [START_REF] Chan | A review of smart homes-Present state and future challenges[END_REF]. The most important targets are to maintain a certain level of independence, to continuously monitor vital parameters, to reduce accidents and to help out with delivering therapy.

Abnormal situations in the behaviour of the person should be detected through the information delivered by smart sensors [START_REF] Scanaill | A Review of Approaches to Mobility Telemonitoring of the Elderly in their Living Environment[END_REF]. Smart homes have demonstrated that measuring the activity of a person at home can be relevant [START_REF] Lebellego | A Model for the Measurement of Patient Activity in a Hospital Suite[END_REF], and also that this monitoring is useful for people with cognitive impairments [START_REF] Bouchard | A Smart Home Agent for Plan Recognition of Cognitively-Impaired Patients[END_REF]. A few studies are related to sound recognition capabilities [START_REF] Stäger | Power and accuracy tradeoffs in sound-based context recognition systems[END_REF], [START_REF] Wang | Robust Environmental Sound Recognition for Home Automation[END_REF].

In our study, a fully functional flat has been fitted with numerous sensors, chosen for classifying the different activities of a person's everyday life. This 47 m s flat is located at the Faculty of Medicine of Grenoble. It includes a kitchen, toilet, a bedroom, a living room and a bath room. It is fitted with: -Presence Infrared sensors (PIR) to approximately determine the location of the subject, -a weather station that provides information on temperature and hygrometry, -open/close detectors placed on communication doors, fridge... -an embedded kinematic sensor, -and, finally, eight microphones (one or two per room) who are in the focus of this paper. Large angle webcams have also been placed but are only used to time-stamp the activities realized in the flat (for machine learning applications) and not as sensors. The microphone setting in the flat is shown in Figure 1; microphones are set on the ceiling and directed vertically to the floor. The sentences uttered by the subject, as well as emitted life sounds, may give valuable information on her usual activities, or on a distress situation.

Data from all these sensors are acquired and processed in real time on four computers disposed in the technical room; they are used as inputs to off-line data fusion algorithms, for detecting and classifying daily activities. An audio analysis system is running on a computer and is delivering information in real time; the detected sound and speech signals are stored for further analysis. With regard to speech signals, an input utterance is recognized and a subsequent process classifies it as normal or distress, by analysing the presence of distress key words. This audio analysis system is presented in section 2, as well as the Autonomous Speech Recognizer (ASR) in section 3; the experiment made in the flat, in order to assess its performances out of "laboratory conditions" is presented in section 4 and the corresponding normal/distress sentence recognition results are given in section 5. Noise suppression techniques in the case of known sources are presented in section 6 and our experiments in section 7; the experimental results are discussed in section 8 before the general conclusion of this study in section 9.

II. THE AUDIO ANALYSIS SYSTEM

Figure 2 depicts the general organization of the audio analysis system; a more detailed description of the system is given in [START_REF] Vacher | Preliminary evaluation of speech/sound recognition for telemedicine application in a real environment[END_REF]. Each microphone is connected to an analog input channel of the acquisition board; each sound is processed independently thanks to a queuing management protocol. The analysis system and the autonomous speech recognizer are running in real time as independent applications on the same GNU/Linux computer. These two applications are synchronized through a file exchange protocol. The system is made of several modules: -acquisition and first analysis,detection, -segmentation, -classification, -and finally, message formatting. They run as independent threads synchronized by a scheduler.

Data acquisition is operated on the 8 input channels simultaneously at a 16 kHz sampling rate by the acquisition and first analysis module. Noise level is evaluated by this module to assess the Signal to Noise Ratio (SNR) of each acquired sound. The SNR of each audio signal is very important for the decision system to estimate the reliability of the corresponding analysis output. The detection module is in charge of signal extraction, i.e. to detect the beginning and the end of the audio event.

The segmentation module is a Gaussian Mixture Model (GMM) classifier that classifies each audio event as everyday life sound or speech. The segmentation module was trained with an everyday life sound corpus [START_REF] Vacher | Sound Classification in a Smart Room Environment: an Approach using GMM and HMM Methods[END_REF] and with the Normal/Distress speech corpus recorded in our laboratory [START_REF] Vacher | Preliminary evaluation of speech/sound recognition for telemedicine application in a real environment[END_REF]. Acoustical features are Linear-Frequency Cepstral Coefficients (LFCC) with 16 filter banks; the classifier uses 24 Gaussian models. These features are used because life sounds are better discriminated from speech with constant bandwidth filters, than with Mel-Frequency Cepstral Coefficients (MFCC), on a logarithmic Mel scale [START_REF] Vacher | Sound Classification in a Smart Room Environment: an Approach using GMM and HMM Methods[END_REF]. Frame width is of 16 ms, with an overlap of 50%.

Then, the signal is transferred by the segmentation module to the speech recognition system in case of speech or to the sound classifier in case of everyday life sounds. Everyday life sounds are classified with a GMM or Hidden Markov Model (HMM) classifier; the classifier is chosen at the beginning of the experiment. Their models were trained with our corpus containing the eight classes of everyday life sounds, using LFCC features (24 filter banks) and 12 Gaussian models.

III. THE AUTONOMOUS SPEECH RECOGNIZER

The autonomous speech recognizer RAPHAEL [START_REF] Vacher | Preliminary evaluation of speech/sound recognition for telemedicine application in a real environment[END_REF] is running as an independent application. It analyzes the speech events resulting from the segmentation module, through a file exchange protocol. As soon as an input file is analyzed, it is deleted, and the 5 best hypotheses are stored in a file. This event allows the scheduler to send the next queued file to the recognizer. Moreover, each sentence file is stored in order to allow for future analysis with different recognition parameters for the recognizer.

The training of the acoustic models was made with large corpora in order to ensure good speaker independence. These corpora were recorded by 300 French speakers in the CLIPS (BRAF100) [START_REF] Vaufreydaz | A new methodology for speech corpora definition from Internet documents[END_REF] and LIMSI laboratories (BREF80 and BREF120) [START_REF] Gauvain | Design Considerations and Text Selection for BREF, a large French read-speech corpus[END_REF]. The language model of this system is a small vocabulary statistical system (299 words in French). This model is obtained using textual information of a current conversation corpus in French. Our main requirement is the correct detection of a possible distress, situation through keyword detection, without understanding the patient's conversation. This conversation corpus contains the sentences in the Normal/Distress speech corpus [START_REF] Vacher | Speech and Sound Use in a Remote Monitoring System for Health Care[END_REF], along with sentences currently uttered during a phone conversation: "Allo oui", "A demain", "J'ai bu ma tisane", "Au revoir"... and sentences that may be a command for a domotic system. The Normal/Distress speech corpus is composed of 126 sentences in French: 66 are typical for a normal situation for the patient: "Bonjour" (Hello), "Où est le sel" (Where is the salt)... , 60 are typical for a distress situation: "Aouh", "Aïe", "Au secours" (Help), "Un médecin vite" (Call a doctor hurry) along with syntactically incorrect French expressions like "Ça va pas bien" (I don't feel good)... The entire conversation corpus is made of 39 domotic orders, 93 distress sentences and usual conversation sentences. Ten samples of each kind are given in Table 1. To validate the system in uncontrolled conditions, we designed a scenario where every subject had to utter 45 sentences (20 distress sentences, 10 normal sentences and 3 phone conversations made up of 5 sentences each). Ten subjects -3 women and 7 men (age: 37.2 ± 14 years, weight: 69 ± 12 kg, height: 1.72 ± 0.08 m)-volunteer for this experiment.

The experiment took place during daytime -hence we did not control the environmental conditions of the experimental session (such as noises occurring in the hall outside the flat). The sentences were uttered in the flat, with the subject sat down or stood up. The subjects were situated between 1 and 10 meters away from the microphones and had no instruction concerning their orientation with respect to the microphones (they could choose to turn their back to the microphone direction). Microphones are set on the ceiling and directed vertically to the floor as shown on Figure 1. The phone was placed on a table in the living room.

The protocol was quite simple. The subjects were asked to first enter the flat and close the door, and then to act a little scenario (close the toilet door, make a noise with a cup and a spoon, let a box fall on the floor and scream "Aïe"). This whole scenario was repeated 3 times for each subject. Then, the subjects had first to go to the living room and close the communication door (between the kitchen and the living room) and then to go to the bed room and read the first half of one of the successions of sentences containing 10 normal and 20 distress sentences. Afterwards, they had to go to the living room and utter the second half of the set of sentences. Each subject was finally called 3 times and had to answer the phone and read the phone conversation given (5 sentences each). To realize these successions of sentences, we chose 30 typical sentences that we randomly scrambled 5 times; then we realized 5 real phone conversations containing 5 successions of sentences, and we picked randomly 3 of the 5 phone conversations.

Every audio signal was recorded by the application, analyzed on the fly and finally stored on the hard disk drive of the computer. For each detected signal, it was first segmented (as sound or speech), and then classified (as one of the eight classes) for a sound, or, in case of a speech event, the 5 more probable hypotheses were stored. For each sound, a XML file was generated, containing the important information.

During this experiment, 2,019 audio signals with an SNR less than 5 dB were not kept; this 5 dB threshold was chosen because of the poor results given by classification and recognition under this value [START_REF] Vacher | Speech and Sound Use in a Remote Monitoring System for Health Care[END_REF]. The number of audio signals collected in this experiment was 3,164 with an SNR of 12.65 ± 5.6 dB.

After classification, we kept 1,008 sounds with a mean SNR of 14.4 ± 6.5 dB and 2,156 sentences. As part of the study presented in this paper, only the recorded sentences are considered. Recorded sounds such as steps, doors clapping were not taken into account in this study. When a sentence was uttered by the speaker, more than one audio signal was recorded by the 7 microphones, depending on his position in the room, but we chose to keep only the signal with the best SNR. At the end, the recorded speech corpus was composed of 429 sentences (7.8 minutes of signal), 7 sentences were not kept because of signal saturation (see Table 2). This corpus was indexed manually because each speaker doesn't follow strictly the instructions given at the beginning of the experiment. Moreover, when two sentences were uttered without a sufficient silence between them, some of these couples were considered as one sentence by the audio analysis system. For these reasons, the number of sentences with and without distress keyword was not the same for each speaker. The 429 sentences were analyzed by the RAPHAEL speech recognizer using the acoustical models and the language model presented in Section 3. These sentences were recorded at various input levels depending on the position of the speaker in the room; therefore, the dynamic of the signal was increased to 50% of the maximal input level for each file below 50% of the maximal level. The language model is made of 299 unigrams, 729 bigrams and 862 trigrams. Afterwards, distress keywords are extracted by a subsequent process from the complete recognized sentences: it is a Missed Alarm (MA) if the uttered sentence is a distress sentence and if there is no distress keyword in the recognized sentence. In the opposite way, it is a False Alarm (FA) if the uttered sentence is a usual conversation sentence or a domotic order sentence and if the recognized sentence contains a distress keyword.

We define the Missed Alarm Rate (MAR), the False Alarm Rate (FAR) and the Global Error Rate (GER) in ( 1) and ( 2 The results are shown as a function of the speaker on Figure 3 and given overall in Table 3. As far as the FAR is concerned, the error is low and then not displayed as a function of the speaker.

Figure 3. GER and MAR as a function of the speaker

The results, MAR and GER, are very dependent on the speaker. For one speaker the MAR is about 5% but for another one, it is above 50%. This speaker uttered distress sentences like a film actor, therefore some sentences are very different from the sentences of the corpus and this leads to an error. For example the French pronoun "je" was not uttered at the beginning of one sentence. For another speaker, a woman, the MAR is upper than 40%. This speaker walked when she uttered the sentences and made noise with their high-heeled shoes, this noise was added to the speech signal. More generally, one distress sentence is "help", this sentence is well recognized if it was uttered with a French pronunciation but not with an English pronunciation because the phoneme [h] doesn't exist in French. When a sentence was uttered in the presence of an environmental noise or after a tongue clicking, the first phoneme of the recognized sentence will be preferentially a fricative or an occlusive and the recognition process may be altered.

VI. NOISE SUPPRESSION IN THE CASE OF KNOWN SOURCES

Sound emitted by a radio or a TV in the HIS ) (n x is a noise source that will be altered by the room acoustic through his transfer function:

( ) ( ) ( ) ( n y n y n e n v - + = ) ( ) ( ) ( ) ( ) ( ) ( n x n h n x n h n e n v * - * + = (3)
These methods may be divided into 2 classes, Least Mean Square (LMS) and Recursive Least Square (RLS) methods. Stability and convergence properties are studied in [START_REF] Michaut | Filtrage adaptatif : théorie et algorithmes[END_REF]. The Multi-delay Block Frequency Domain (MDF) algorithm is an implementation of the LMS algorithm in the frequency domain [START_REF] Soo | Multidelay block frequency domain adaptive filter[END_REF]. This algorithm is implemented in the SPEEX library under GPL License [START_REF] Valin | On adjusting the learning rate in frequency domain echo cancellation with double talk[END_REF] for echo cancellation system.

In echo cancellation systems, the presence of audio signal e(n) (double-talk) tends to make the adaptive filter diverge. To prevent this problem, robust echo cancellers require adjustment of the learning rate to take the presence of double talk in the signal into account. Most echo cancellation algorithms attempt to explicitly detect double-talk but this approach is not very successful, especially in presence of a stationary background noise. A new method [START_REF] Valin | A new robust frequency domain echo canceller with closed-loop learning rate adaptation[END_REF] was proposed by the authors of the library, where the misalignment is estimated in closed-loop based on a gradient adaptive approach; this closed-loop technique is applied to the block frequency domain (MDF) adaptive filter. The echo cancellation technique used introduces a specific noise into the v(n) signal and a post-filtering is requested. The method implemented in SPEEX is Minimum Mean Square Estimator Short-Time Amplitude Spectrum Estimator (MMSE-STSA) presented in [START_REF] Ephraim | Speech enhancement using a minimummean square error short-time spectral amplitude estimator[END_REF]. The STSA estimator is associated to an estimation of the a priori SNR. The formulated hypothesis are following: -added noise is Gaussian, stationary and the spectral density is known, -an estimation of the speech spectrum is available, -spectral coefficients are Gaussian and statistically independent, -the phase of the Discrete Fourier Transform follows a uniform distribution law and is amplitude independent. Some improvements are added to the SNR estimation [START_REF] Cohen | Speech enhancement for non-stationary noise environments[END_REF] and a psycho-acoustical approach for postfiltering [START_REF] Gustafsson | A psychoacoustic approach to combined acoustic echo cancellation and noise reduction[END_REF]; the purpose of this post-filter is to attenuate both, the residual echo remaining after an imperfect echo cancellation and the noise without introducing "musical noise", i.e. randomly distributed, time-variant spectral peaks in the residual noise spectrum as spectral subtraction or Wiener rule does [START_REF] Vaseghi | Advanced Signal Processing and Digital Noise Reduction[END_REF]. The post-filter is implemented in the frequency domain, which basically means that the spectrum of the input signal is multiplied by weighting coefficients calculated according to a weighting rule; their values are chosen by taking into account auditory masking. Noise is inaudible if it is too close to the useful signal in frequency or time; therefore noise components which lie below the masked threshold of the ear are inaudible and can thus be left unchanged. This method leads to more natural hearing and to less annoying residual noise VII. NOISE SUPPRESSION EXPERIMENTS Two microphones were set in a room, the Reference Microphone in front of the Speaker System in order to record music or radio news (France-Info, a French radio broadcasting news all the day) and the Signal Microphone in order to record a French speaker uttering sentences in the room as shown on Figure 5. The two microphones are connected to the Audio Analysis System in charge of echo-cancellation; the resulting signal after echo-cancellation with or without post-filtering is then sent to the ASR and stored for further analysis. For this experiment the French speaker is standing in the center of the recording room, he is not facing the signal microphone. He has to speak with a normal voice level, the power level of the radio is set to be rather strong and then the SNR may be approximately 0 dB.

Another way is to record separately the reference and the noise after propagation in the room. The speech signal may then be added to the resulting noise at different SNR levels; the Normal/Distress corpus recorded during previous studies [START_REF] Vacher | Speech and Sound Use in a Remote Monitoring System for Health Care[END_REF] may be used for this purpose. Echo-cancellation is operated in batch accorded to the reference and the addition of speech and noise. Hence, it is possible to proceed with the same signal using different settings of the echo-canceller. The results obtained with these two approaches are presented in the next section.

The spectrogram and the corresponding signal are displayed on Subfigure 6b in the case of the sentence "J'ai besoin d'une infirmière" and the corresponding noised signal at -6dB SNR on Subfigure 6a. After echo-cancellation the noise level remain quite significant as shown on Subfigure 6c but words are separated. The corresponding spectrogram shows that noise is present in all the frequency bands. By using postfiltering in conjunction with echo-cancellation, noise is low, as shown on Subfigure 6d, but the high frequencies of the original signal are attenuated. Harmonic components are not changed. It is then possible that speech recognition may be altered; it will be different according to the phonemes making up the sentence. In the absence of echo-cancellation, distress keywords are badly recognized, the MAR is fast increasing since +10 dB. The MAR curve is nearly flat between -3 dB and + 12 dB when echo-cancellation is processed, the post-filtering doesn't improve significantly speech recognition in this interval, the MAR is even greater at +12 dB. On the contrary, post-filtering is important below -6 dB and allows the MAR to be 55% (78% for echo-cancellation alone).

The echo-cancellation system was tested with the same corpus with 2 different noise sources: classic music (The 3rd symphony opus 55 by Beethoven) and pop music (Artificial Animals Riding on Neverland by AaRON). Results are displayed on Figure 8, the error rate increases linearly with noise level. This kind of noises, and especially pop music, are more difficult to suppress because of the presence of large band sources like percussion instruments.

In complement, 4 speakers (3 men, 1 woman, between 22 and 55 years old) uttered 20 distress sentences of the Normal/Distress corpus in the recording room, this process was operated by the speaker 2 or 3 times. The echo-cancellation was operated in real-time by the Audio System analysis. The level of the radio France-Info was set in order to achieve a 0 dB SNR level, each speaker was standing in the center of the recording room. The MAR, global for all the speakers, is 27%. The results depend on the voice level of the speaker during this experiment and on the speaker himself. Resulting noise at the beginning and at the end of the sentence alters the recognition; it may then be useful for detecting these 2 moments with a good precision to use shorter silence intervals. This paper presents the results of distress situation detection from speech in a smart room in the context of telemonitoring for elderly people at home. The first experiment presented was realized in a HIS flat where 10 speakers were involved and played a scenario. Our main requirement is the correct detection of a possible distress situation through keyword detection, without understanding the patient's conversation. Each speaker uttered about 45 sentences of the Normal/Distress speech corpus, along with sentences currently uttered during a telephone conversation. The Global Error Rate is 15.6%, the performance of the ASR may be improved in future studies by taking into account sounds emitted in spontaneous speech: tongue clicking, hesitatingly speaking and phonetical variants.

The second experiment is related to radio noise cancellation. Four speakers were involved in this experiment and uttered distress sentences during the listen of the news on France-Info. The Missed Alarm Rate was 27%. Some improvements must be added to the Echo-Cancellation method when the noise source is not speech but music.

Our future work will be the study of aging voice because most of the patients or elderly people at home are more than 70 years old. Moreover, the characteristics of their voice are very different and must be studied in order to detect distress or assisting requirement from the speech.

Figure 1 .

 1 Figure 1. Microphone setting in the flat on the left and full sensors set-up on the right

  ), n referring to the 'number of', DS to 'Distress Sentence', NS to 'Normal Sentence'.

  of the room acoustic and * the convolution operator. It is then superposed to the signal ) (n e emitted in the room: speech uttered by the patient or everyday life sound. The signal recorded by the microphone in the HIS is then) ). Various methods were developed in order to suppress the noise[START_REF] Michaut | Filtrage adaptatif : théorie et algorithmes[END_REF], some methods to obtain an estimation ) ( ˆn h of the impulse response of the room acoustic in order to remove the noise as shown on Figure4.The resulting output is given in (3).

)

Figure 4 .

 4 Figure 4. Block Diagram of Echo Cancellation System

Figure 9 .

 9 Figure 9. Missed Alarm Rate with Echo-Cancellation in Real-Time as a function of the speaker

TABLE 1 .

 1 THE CURRENT CONVERSATION CORPUS

	Sample	Domotic Order Sentence	Distress Sentence	Usual Conversation Sentence
	1	Allume la lumière		A l'aide	Allô c'est moi
	2	Éteins la lumière		Je suis tombé	Allô c'est qui
	3	Ferme la porte		Une infirmière vite	Bonjour Monsieur
	4	Ouvre la porte		Appelez une ambulance	Dehors il pleut
	5	Fermez les volets		Aïe aïe aïe	Euh non
	6	Ouvrez les volets		Je ne peux plus bouger	J'ai bu du café
	7	Il fait très chaud		Je ne me sens pas bien du tout	J'ai fermé la fenêtre
	8	Il fait très froid		Je me sens très mal	J'ai sommeil
	9	J'ai très chaud		J'ai mal	Tout va bien
	10	J'ai très froid		J'ai de la fièvre	A demain
	8 microphones	Acquisition and First Analysis	Analysis System	Set Up Module Scheduler Module	Speech Autonomous
		Detection	Segmentation	Speech	Recognizer (RAPHAEL)
					Sound	Sound Classifier
	XML Output	Keyword and Sound Class Extraction / Message Formatting
		Figure 2. Overview of the Audio Analysis System

TABLE 2 .

 2 EXPERIMENTAL RECORDED CORPUS: BEST SNR

		SENTENCES
	Speaker	Sentences	with	Sentences
	Identifier	distress keyword	without distress
		(197)		keyword (232)
	N°1	21		24
	N°2	19		25
	N°3	20		23
	N°4	18		24
	N°5	20		22
	N°6	19		24
	N°7	17		23
	N°8	21		20
	N°9	21		24
	N°10	21		23
	V. NORMAL/DISTRESS SENTENCE RECOGNITION

TABLE 3 .

 3 DISTRESS KEYWORD PERFORMANCE FOR THE EXPERIMENTAL CORPUS

		MAR	FAR	GER
	Error Rate	29.5%	4%	15.6%

VIII. ECHO-CANCELLATION EVALUATION

The reference signal and the resulting noise in the room were recorded by the 2 microphones during 30 minutes at 16 kHz sampling rate. 126 sentences uttered by one speaker were extracted from the Normal/Distress corpus and mixed with the resulting noise at 9 SNR levels: -12, -9, -6, -3, 0, 3, 6, 9 and 12 dB. Each SNR level was obtained by adjusting the level of both the recorded noise and the audio file of the corpus. The resulting signal is then processed by the echo-cancellation system and the 126 sentences were extracted and sent to the ASR. This process is iterated a second time by the echocancellation system with post-filtering. The language model of the ASR was a medium vocabulary statistical system (9,958 words in French). This model is obtained by extraction of textual information from the Internet and from the French newspaper "Le Monde". Then, it is optimized using our conversation corpus (refer to Table 1). The recognition results for these two processing methods are presented on Figure 7. The buffer size of the algorithm was 256 samples in order to improve the processing time; the filter size was 8192 samples enough to take into account the size of the room and the delay after reverberation on walls and windows. 3.5 m 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000


Margrét Florent Cabric
email: forent.cabric@inria.fr

Vilborg Bjarnadóttir

Anne-Flore Cabouat
email: anne-fore.cabouat@inria.fr

Petra Isenberg
email: petra.isenberg@inria.fr

Margr Ét

Vilborg Bjarnad
email: mbjarnad@umd.edu

Open Questions about the Visualization of Sociodemographic Data

Keywords:

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

INTRODUCTION

Data visualizations are commonly based on an abstraction of data according to some criteria, such as an average or a clustering. These abstractions enable us to make comparisons, for instance, between multiple groups or to identify outliers that do not match the characteristics of the group. When we visualize data about people, this kind of abstraction may lead to the promotion of stereotypes and biases. Stereotypes are built on grouping together people with presumed shared characteristics as a means to describe group membership, contrast that group with others, and identify outsiders [START_REF] Becker | Outsiders: Studies in the Sociology of Deviance[END_REF][START_REF] Nelson | Handbook of prejudice, stereotyping, and discrimination[END_REF][START_REF] Susie | Challenging Gender Stereotypes in the Early Years[END_REF]. Abstraction can also lead to invisibilizing marginalized groups and their issues [START_REF] Herzog | Invisibilization and silencing as an ethical and sociological challenge[END_REF][START_REF] Rall | Data visualization for human rights advocacy[END_REF][START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF]. Thus, the process of abstraction of data in visualizations can be very harmful -for example, by invisibilizing marginalized groups, leading to biased decisions, or conveying a feeling of not being included. The design of nonharmful visualization raises many design [START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF] and ethical [START_REF] Correll | Ethical dimensions of visualization research[END_REF] questions.

Our work is in line with previous work on how to visualize data about people [START_REF] Correll | Ethical dimensions of visualization research[END_REF][START_REF] Dhawka | We are the data: Challenges and opportunities for creating demographically diverse anthropographics[END_REF][START_REF] Dörk | Critical infovis: Exploring the politics of visualization[END_REF][START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF]. In particular, we focus on expanding on the "Do No Harm Guide" [START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF] that discusses a set of considerations for designing visualizations considering diversity, equity, and inclusion. We elaborate on this work through a discussion of open research questions. Our work is a refection on the challenges that researchers in the community need to tackle to effectively and equitably visualize sociodemographic data in a nonharmful way and an opportunity to discuss our current practices.

PROTECTION AND VISUALIZATION OF SOCIODEMO-GRAPHIC DATA

The protection of private attributes has a long history but has seen rapid changes in recent decades. The number of policies regarding the protection of sociodemographic data is growing across companies, states, and institutions. One such policy, the EU's General Data Protection Regulation (GDPR) [START_REF]Article 9 -regulation[END_REF], strongly regulates the processing of fve categories of sensitive data: personal, trade-union membership, genetic or biometric, health-related, and sex life or sexual orientation. The UK [START_REF] Uk | Types of discrimination ('protected characteristics[END_REF] Several papers have discussed broader issues around the visualization of data about people. The belief that data visualizations might be neutral has been disparaged by many: Visualizations are political [START_REF] Dörk | Critical infovis: Exploring the politics of visualization[END_REF], can promote empathy [START_REF] Boy | Showing people behind data: Does anthropomorphizing visualizations elicit more empathy for human rights data?[END_REF], often do not surface inherent uncertainty [START_REF] Hullman | Why authors don't visualize uncertainty[END_REF], and have even been called inhumane [START_REF] Dragga | Cruel pies: The inhumanity of technical illustrations[END_REF]. Beyond the visualization, data and its collection and processing are also regularly prone to biases. Criado Perez [START_REF] Perez | Invisible Women: Data Bias in a World Designed for Men[END_REF] has summarized how unrepresentative data collection about people (for example, only collecting data from men) has led to overgeneralizations to the general population and thus has caused discrimination and other problems such as discomfort in women's daily lives [START_REF] Perez | Invisible Women: Data Bias in a World Designed for Men[END_REF]Chapter 8] or medical complications [START_REF] Perez | Invisible Women: Data Bias in a World Designed for Men[END_REF]Chapter 10].

Out of the above-mentioned personal attributes, a few have been the subject of dedicated papers on how to visualize them in a nonharmful way. Disability has gained attention from visualization researchers, with most work focused on making data visualization accessible to people with disabilities [START_REF] Elavsky | How accessible is my visualization? evaluating visualization accessibility with chartability[END_REF][START_REF] Elmqvist | Visualization for the blind[END_REF][START_REF] Marriott | Inclusive data visualization for people with disabilities: A call to action[END_REF][START_REF] Wu | Understanding data accessibility for people with intellectual and developmental disabilities[END_REF]. Only a few studies have explored how to represent data about people with disabilities. For example, Barstow and colleagues [START_REF] Barstow | Examining perceptions of existing and newly created accessibility symbols[END_REF] investigated the design of inclusive visual symbols beyond the "traditional" wheelchair icon, which fails to represent many people with a physical disability who need specifc access or consideration. Race or ethnicity has received attention regarding the design of nonharmful visualization. Schwabish and Feng [START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF] discussed a set of considerations for data collection, use of language, visibility of data, and use of colors, icons, and shapes. The authors emphasize talking to people to ensure they are represented correctly and harmlessly in data visualizations. Dhawka and colleagues [START_REF] Dhawka | We are the data: Challenges and opportunities for creating demographically diverse anthropographics[END_REF] explored the challenges and opportunities of representing diverse people grouped by race using anthropographics. Gender has also been the subject of studies related to the diversity of the community itself [START_REF] Tovanich | Gender in 30 years of IEEE visualization[END_REF]. Papers on the visual representation of gender as a demographic variable are rare. In a blog post, Muth [START_REF] Muth | An alternative to pink & blue: Colors for gender data[END_REF] collected multiple examples of professional visualizations of gender by institutions such as Bloomberg and the New York Times. The visualizations used a variety of colors, including pink/blue, but also many other combinations, such as orange/green or green/purple, without a clear consistency. Finding the right visual variables, layouts, and marks has been shown to be challenging for these sociodemographic attributes (disability, ethnicity, and gender), and many others may surface as more of the protected attributes are explored. However, multiple personal attributes raise common open research questions that will be discussed next.

SOME OPEN QUESTIONS ABOUT THE VISUALIZATION OF SOCIODEMOGRAPHIC DATA

As pointed out in the introduction, visualizations are often inherently built on a process of abstraction. When data about people have been abstracted into groups, these groups need to be assigned marks, visual variables, and a layout. This visualization design process involves choices that may refect a certain way of thinking or "not thinking" [START_REF] Perez | Invisible Women: Data Bias in a World Designed for Men[END_REF]Preface], built-in default encodings, or the use of over-general guidelines. Visualization challenges in this process are related to the potential amplifcation of stereotypes and the marginalization of groups of people. In addition, designers have the diffcult task of balancing how the visualized populations will see themselves refected in the visualization and the ability of decision-makers to use the visualization effectively. We next outline open questions we would like to discuss as part of the Vis4Good workshop.

Balancing Effciency

Visual decision-making tasks are cognitively complex: The reader must understand the visualization, defne the underlying decision problem, compare different options, and then make one choice [START_REF] Dimara | A critical refection on visualization research: Where do decision making tasks hide?[END_REF]. Such complexity may require balancing inclusive visual design with the need for time-effcient task completion. Air traffc control, crowd management, or emergency medicine regularly require decisionmakers to complete critical tasks quickly and accurately. To reduce the time taken to make a decision, people can rely on stereotypes to activate heuristics [START_REF] Bodenhausen | Stereotypes as judgmental heuristics: Evidence of circadian variations in discrimination[END_REF][START_REF] Bodenhausen | Effects of stereotypes on decision making and information-processing strategies[END_REF], sometimes at the cost of accuracy [START_REF] Payne | Adaptive strategy selection in decision making[END_REF]. This Speed-Accuracy Trade-off (SAT) [START_REF] Larson | Speed-accuracy tradeoffs in decision making: Perception shifts and goal activation bias decision thresholds[END_REF] has been discussed for a long time in psychology [START_REF] Gigerenzer | Heuristic decision making[END_REF]. In the context of visualization, we are interested in discussing the impact of stereotyped visual representations on the decision-making processes.

Creating visualization with stereotyped attributes such as red and blue colors for gender may speed up cognitive tasks and decisions. 2 and consider the balance between information and visualization simplicity and how included different types of viewers may feel. The data is from the UK census and uses its data labels [START_REF]Census 2021 -ethnic group[END_REF]. Note: the y-axis is broken between the 5.5M and 50M ticks.

Studies have assessed the effectiveness of using semantically resonant colors [START_REF] Lin | Selecting semantically-resonant colors for data visualization[END_REF]. For example, using a color associated with the data represented, such as yellow for a bar representing bananas. In contrast, there is some evidence that using a reversed mapping -associating a visual mapping that is typically associated with a specifc group (brighter colors = women) to another group (brighter colors = men) -slows down classifcation and association tasks [START_REF] Semin | Why the bride wears white: Grounding gender with brightness[END_REF]. We are not aware of studies that evaluated the effciency of stereotyped and unstereotyped visualizations for common tasks [START_REF] Brehmer | A multi-level typology of abstract visualization tasks[END_REF]. Suppose previous results on semantically resonant colors are confrmed with stereotyped visualizations. In that case, a new dilemma will arise for designers of visualizations used in situations that require quick and accurate task completion: Is the effciency of my visualizations worth the use of stereotyped data encodings? One may argue that this question is not even worth asking because the well-being of the people represented should trump concerns about effciency. Yet, people's well-being might also depend on quick and correct decisions. What has not been studied to date is: To what extent do the potential benefts of stereotyped visualizations matter? Perhaps the effciency benefts are so small that we should never consider using stereotyped visualizations, and research should focus on raising awareness of stereotypes. Or, perhaps the benefts are signifcant enough to motivate future works on how to avoid stereotyped visualizations while still achieving the same effciency. For example, training people to associate groups with non-stereotyped visual variables such as colors may solve the problem; but how and to what extent to invest in such training in different scenarios is an open question. To be clear, we do not call for the use of stereotyped visual encodings. Instead, we call for more research to fully understand the effects of using undesired stereotyped visualizations.

Balancing Simplicity

In their "Do No Harm Guide," Schwabish and Feng [START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF] recommend that "data visualizations should use more complex designs if that would more accurately refect and promote a better understanding of the topic being shown." In addition, Baumer and colleagues [START_REF] Baumer | Of course it's political! a critical inquiry into underemphasized dimensions in civic text visualization[END_REF] argue that complexity can lead to inclusivity and help data visualization readers to gain deeper insights. We relate complexity here to the number of data categories being displayed. To facilitate data exploration, designers must disaggregate and split specifc data dimensions into different factors and subfactors. For instance, the 2021 UK Census dataset [START_REF]Census 2021 -ethnic group[END_REF] proposed three levels of disaggregation for ethnicity data: 5 (see Fig. 1), 20 (see Fig. 2), and 288 categories. 1 and consider the balance between information and visualization simplicity and how included different types of viewers may feel. The data is from the UK census and uses its data labels [START_REF]Census 2021 -ethnic group[END_REF]. Note: the y-axis is broken between the 3.6M and 40M ticks.

On the one hand, the representation of a small number of groups can result in the invisibilization of small and sometimes marginalized groups. Invisibilizing groups can reinforce the feeling of being outside society for the people represented [START_REF] Herzog | Invisibilization and silencing as an ethical and sociological challenge[END_REF]. On the other hand, hypervisibilizing groups [START_REF] Settles | Scrutinized but not recognized: (In)visibility and hypervisibility experiences of faculty of color[END_REF] can be interpreted as tokenization of the people represented [START_REF] Wingfeld | When visibility hurts and helps: How intersections of race and gender shape black professional men's experiences with tokenization[END_REF] and lead to increased surveillance from the main group. Overrepresentation in the data or visualization can reinforce feelings of marginalization.

The continuum of disaggregation is vast, and visualization designers must choose between simplicity, which can lead to overgeneralization and invisibilization, or better representativity of the population, which can result in hypervisibilization and visual clutter. Minimizing the number of categories can considerably reduce the number of items to process and, thus, facilitate data exploration and decision-making. Representing the 288 ethnic groups of the UK census [START_REF]Census 2021 -ethnic group[END_REF] can give a sense of representativeness of the population but make the visualization less time-effcient for tasks that require a lot of attention and carefulness. This trade-off between simplicity and better representation raises an important question: To what extent can better representation of people from marginalized groups reduce effciency or lead to biased decisions that may harm the people represented?

To be clear again, we are not advocating the invisibilization of groups in data visualizations. On the contrary, we want visualization researchers to invest time in understanding the effects of this trade-off on tasks as complex as decision-making. For example, it would be interesting to study tools that help decision-makers explore different disaggregation levels while remaining simple. One notable example is VisPilot [START_REF] Lee | Avoiding drill-down fallacies with VisPilot: Assisted exploration of data subsets[END_REF], which supports the exploration of subgroups while avoiding drill-down fallacies that lead to incomplete insights. It should be noted that many visualizations, in particular those designed for the general public, do not require the same type of effciency as discussed above. Instead, designers should focus on other objectives, such as the representativity of the people visualized, understandability, or message reception.

Few previous works in the visualization community have discussed the effect of grouping [START_REF] Baumer | Of course it's political! a critical inquiry into underemphasized dimensions in civic text visualization[END_REF][START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF]. Future studies can take inspiration from other research domains, such as the social sciences [START_REF] Herzog | Invisibilization and silencing as an ethical and sociological challenge[END_REF][START_REF] Settles | Scrutinized but not recognized: (In)visibility and hypervisibility experiences of faculty of color[END_REF], to develop new methods to investigate how people feel harmed or represented when looking at visualizations under different levels of aggregation.

Inclusiveness of Different Representation Types

Some visualization researchers have raised the question of how best to represent the individuals behind sociodemographic data. Recently, researchers have studied visual representations to show the diversity [START_REF] Dhawka | We are the data: Challenges and opportunities for creating demographically diverse anthropographics[END_REF] or disparity [START_REF] Holder | Dispersion vs disparity: Hiding variability can encourage stereotyping when visualizing social outcomes[END_REF] of people inside a group. Morais and colleagues defned three levels of granularity to represent individuals [START_REF] Morais | Showing data about people: A design space of anthropographics[END_REF]:

Low: The marks aggregate all people from the same group to hide variability (see Fig. 1 and Fig. 2). Intermediate: The marks visualized represent a fxed number of people (greater than 1; see Fig. 3) Maximum: Each mark is associated with one individual. This continuum poses interesting challenges for the visualization community. First, it is unclear how people represented in the data are affected by specifc types of visualization or how they prefer to be represented. Second, it is unclear how a specifc representation affects the stakeholders' understanding of the underlying data.

Holder and Xiong [START_REF] Holder | Dispersion vs disparity: Hiding variability can encourage stereotyping when visualizing social outcomes[END_REF], for example, showed that the type of representation mattered. The authors compared visualizations of low granularity (bar charts, dot plots) with those of higher levels of granularity (jitter plots, prediction intervals). The authors found low granularity visualizations increased stereotyped conclusions.

The representation of intermediate or maximum levels of granularity seems promising. However, visualizing individuals has multiple challenges, including the scalability of maximum granularity to a large dataset [START_REF] Morais | Showing data about people: A design space of anthropographics[END_REF], and fnding an adequate unit size, in particular when representing groups with large differences in population size. In Fig. 3, we decided on a unit size that allowed a more refned representation of smaller groups but made it diffcult to fully represent the largest group within a suitable layout. Perhaps early work on focus+context visualization should be revisited in this context [START_REF] Tominski | Interactive lenses for visualization: An extended survey[END_REF].

A self-perception of the individual in data visualization can also be achieved with anthropographics -data visualizations that use human-shaped graphical representation. Researchers studied such visualizations to elicit more empathy for human rights [START_REF] Boy | Showing people behind data: Does anthropomorphizing visualizations elicit more empathy for human rights data?[END_REF] and promote prosocial behavior [START_REF] Morais | Can anthropographics promote prosociality? A review and large-sample study[END_REF]. Despite initial results that did not show clear benefts of anthropographics, this stream of research is growing. Notably, Dhawka and colleagues [START_REF] Dhawka | We are the data: Challenges and opportunities for creating demographically diverse anthropographics[END_REF] recently investigated the challenges of representing the diversity of people's races. In particular, they identifed the mapping between sociodemographic groups and visual channels as one that can lead to problematic choices, depending on whether or not the designer is aware of the social construction of groups and the stereotypes that can exist between the group and the visual channel. In the context of Virtual Reality, Ivanov and colleagues [START_REF] Ivanov | A walk among the data[END_REF] investigated individual representations of 3D human fgures who died in mass shootings. The authors rendered human fgures from six unique models as fat-grey silhouettes according to age and gender. Anthropographics, as a new paradigm in visualization , requires further studies to understand its power, in particular, to promote diversity, equity, and inclusion.

Anthropographics per se requires grouping and visualizing people based on a physical characteristic shared by an entire group. In the "Do No Harm Guide" [START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF], Schwabish and Xiong advised choosing unstereotyped icons to represent people. However, grouping people based on a physical characteristic requires that the entire group shares the physical characteristic and that this characteristic is not a topic of discrimination. Anthropographics raises many questions, including: To what extent is it even possible to represent people based on a physical characteristic that is not stereotyped? Also, to what extent does showing certain types of human-shaped icons lead to feelings of inclusion or exclusion?

Summary

As mentioned in Section 2, visualizations are not neutral. They can lead to biased decisions, the perpetuation of stereotypes, the reinforcement of discrimination, or the amplifcation of groups' marginalization. Visualizing demographic data has the potential to cause great harm, thus requiring careful and responsible design choices. Visualization designers can select what data they want to show, the visual marks mapped to each category, and what extra information to disclose. Such decisions place the designer in charge of how the visualization can be used or interpreted [START_REF] Correll | Ethical dimensions of visualization research[END_REF] and, consequently, infuencing the people visually represented. Designers must be aware that any design choice involves trade-offs between reducing potential harm and, for example, time effciency, visual simplicity, or efforts to elicit empathy.

We know little about the potential side effects of designing nonharmful visualizations. The set of open questions we raised focused on the trade-off between the use of visual stereotypes and effciency, the trade-off between the inclusiveness of the marginalized group represented and simplicity, and the right level of individuation and perception to convey a sense of inclusion. But many other important questions remain open, including the effciency of anthropographics or the scalability of unit-based visualizations.

DISCUSSION

At the beginning of this paper, we introduced protected attributes as important data dimensions focus on in order to accurately represent individuals and groups of people and their characteristics. Overall, there is very limited visualization research on this topic, and until now, it has been mainly focused on three of these attributes: gender [START_REF] Muth | An alternative to pink & blue: Colors for gender data[END_REF], disability [START_REF] Barstow | Examining perceptions of existing and newly created accessibility symbols[END_REF], and ethnicity/race [START_REF] Dhawka | We are the data: Challenges and opportunities for creating demographically diverse anthropographics[END_REF]. We have not come across any dedicated discussion on the representation of religion, age, marital status, or any of the other attributes. The question is, why? Are some of these attributes less associated with visual stereotypes than others? For example, stereotyped colors come to mind immediately when we think of gender; conversely, fewer and weaker visual stereotypes may come to mind when considering age.

As researchers from both the US and Europe, we also are aware of the continental and national differences in calls for social changes broadly linked to diversity, equity, and inclusion. For example, a person's veteran status is a protected attribute in the US, while it is not in the UK or Canada. As such, studies on how visualizations of sociodemographic data are perceived should be a multi-cultural effort to study how and if perceptions are local.

Including people from marginalized populations throughout the design process is an important step in mindful representations of such groups -as suggested in recent works on demographic and nonharmful visualization [START_REF] Barstow | Examining perceptions of existing and newly created accessibility symbols[END_REF][START_REF] Baumer | Of course it's political! a critical inquiry into underemphasized dimensions in civic text visualization[END_REF][START_REF] Dhawka | We are the data: Challenges and opportunities for creating demographically diverse anthropographics[END_REF][START_REF] Dörk | Critical infovis: Exploring the politics of visualization[END_REF][START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF]. For example, Mack and colleagues [START_REF] Mack | Towards inclusive avatars: Disability representation in avatar platforms[END_REF] interviewed 18 people with disabilities or related identities across two months to understand their perception, design choices, and use of avatars on online platforms. Respondents expressed the need to modify the avatar's disclosure of their disability according to context or environment. The choice of disclosing (or not) or aggregating (or not) personal data or attributes in visualizations should be made in consultation with the persons represented.

Visualization designers should also consider disclosing design rationales and information about the people involved in the design process as an integral part of a visualization [START_REF] Burns | Making the invisible visible: Risks and benefts of disclosing metadata in visualization[END_REF]. Dörk and colleagues [START_REF] Dörk | Critical infovis: Exploring the politics of visualization[END_REF] highlight the importance of empowering the reader by providing background information about the data shown, the data hidden, and the intention of the visualization designer. Recently, Burns and colleagues [START_REF] Burns | From invisible to visible: Impacts of metadata in communicative data visualization[END_REF] showed that disclosing metadata, such as design rationales (for instance, why the designer mapped a specifc visual variable to a category) or data sources (where the data comes from) can improve transparency and viewer's understanding.

Many topics related to sociodemographic data visualization require a cross-disciplinary approach. For instance, inclusiveness of data collection requires collaboration between scientists and institutions that collect data. Visualization researchers also have a role to play in designing innovative data collection methods. For example, Beischel and colleagues [START_REF] Beischel | Visualizing gender/sex diversity via sexual confgurations theory[END_REF] designed a visual tool to let participants express their sex and gender based on multiple dimensions.

CONCLUSION

Visualizing sociodemographic data requires designers to address many design challenges, from data collection to visualization. The design of visualization is not neutral and can convey different messages with the same underlying data. Sometimes, these messages can harm people, in particular, people from marginalized groups, and we need to understand these potential harms better. Through a set of open questions, this paper aims to advance the discussion on how to design the least harmful visualization.

Many of the questions we raise represent current considerations in the visualization community and in society. It should be noted that all the constructs that can harm people (invisibilization, hypervisibilization, stereotypes in representation, bias in decision-making) are socially and contextually dependent. Therefore, inclusive visualization practices may always be a moving target. When we base these visualizations on key principles, progress can be achieved, a point made well in the "Do No Harm Guide" [START_REF] Schwabish | Do No Harm Guide: Applying equity awareness in data visualization[END_REF]. We have an obligation to be careful and inclusive, but, more critically, additional research is needed to understand better how our design choices affect both the people represented and the people using our visualizations.

FIGURE CREDITS

We, as authors, state that all of our own fgures are and remain under our own personal copyright, with permission to be used here. We also make them available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license and share them at osf.io/a2u9c

Figure 2 :

 2 Figure 2: England's and Wales's residents grouped into 20 different ethnic groups. Contrast this fgure with Fig.1and consider the balance between information and visualization simplicity and how included different types of viewers may feel. The data is from the UK census and uses its data labels[START_REF]Census 2021 -ethnic group[END_REF]. Note: the y-axis is broken between the 3.6M and 40M ticks.

  Figure3: Unit-based visualization from the UK census[START_REF]Census 2021 -ethnic group[END_REF], where each full square represents 250,000 UK residents by ethnic group. While a unit-based visualization may be benefcial to show that each group is made up of individuals, our choice of unit size doesn't allow full representation of the White group.

			250,000
					+ 171
		Black	Mixed or
	Asian	Caribbean	Multiple	Other ethnic	White
				group
		African	ethnic groups

ACKNOWLEDGMENTS

The authors wish to thank Stephanie Manuzak for proofreading the frst version of the paper. This work was supported in part by the Action Exploratoire (AeX) EquityAnalytics from Inria.