Talking Like One of Us: Effects of Using Regional Language in a Humanoid Social Robot

In plain words

Social robots are starting to work in public service jobs, and how they talk shapes whether people accept them. This paper asks whether it matters if a robot speaks a regional language instead of the standard one. The authors used the humanoid robot Pepper, first in standard High German and then in Low German, a regional language that people in northern Germany still understand. The robot said the same things in both versions.

Each participant talked with the robot in both versions, in an order that varied from person to person. Afterwards they rated the robot on a short list of adjectives grouped into three feelings: warmth, competence and discomfort.

The main finding is that the Low German version made the robot feel warmer. Competence did not change. The change in discomfort was small and not statistically clear. The study is small, so treat it as an early signal rather than a settled answer.

TL;DR

Within-subject study with 17 participants in which Pepper gave the same dialogue in High German and Low German. Perceived warmth was significantly higher for Low German (p = 0.027). Competence (p = 0.378) and discomfort (p = 0.056, slightly lower for Low German) did not reach significance. The paper reports no effect sizes and does not name the t-test variant. No official code was found.

Problem

Robots are increasingly used in public service settings, and acceptance depends on how they communicate §1. The paper asks whether a regional language variety changes how people perceive a social robot in terms of warmth, competence and discomfort, compared with the standard language §2. The authors state that linguistic variation in a cultural context has not been well studied in human-robot interaction §2. Related work reports mixed results: one study of regional speech variants found effects that depended on whether participants spoke dialect themselves §3. Without a controlled comparison of this kind, a designer has no evidence on whether switching dialect changes how a robot is judged.

Method

This is an empirical user study, not a new system. Pepper runs the same dialogue twice, once in High German and once in Low German §5.1. Pepper's German language package handles recognition and dialogue, and its German synthetic voice produces the Low German speech, which the authors describe as clearly recognisable as Low German but not perfect §4. The dialogue is written as QiChat topics, which are sets of rules, concepts and proposals that set the conversation flow §4 fig. 2.

The steps below follow the protocol in §5.1.

1. Before the session, each participant completes a questionnaire on gender, age, place of origin and knowledge of Low German §5.1.
2. Participants are split so that nearly half start in Low German and the rest start in High German, which limits ordering effects §5.1.
3. The robot greets the participant, asks for their name and then asks mostly closed questions on several topics. It repeats the participant's name during the conversation and answers a few predefined questions §5.1. The topic, content and course are the same in both versions §5.1.
4. Participants speak High German in both sessions, because the robot does not understand Low German §5.1.
5. After each dialogue, the participant rates the 18 items of the Robotic Social Attributes Scale (RoSAS) on a 5-point scale. The scale comes from Carpinella et al., and its items draw on the Godspeed scale §5.
6. Each factor is the sum of its six items, so it ranges from 6 to 30 §5.1. Warmth: happy, feeling, social, organic, compassionate, emotional. Competence: capable, responsive, interactive, reliable, competent, knowledgable. Discomfort: scary, strange, awkward, dangerous, awful, aggressive §5.1.
7. For each factor, a t-test compares the High German and Low German scores at significance level 0.05 §5.2.

# Within-subject dialect study  [§5.1]
# Input:  participants (17); variants = {high_german, low_german}
# Output: one RoSAS rating set per participant and variant

def run_study(participants, variants):
    # Pre-session questionnaire: gender, age, origin, Low German knowledge  [§5.1]
    for participant in participants:
        background[participant] = questionnaire(participant)
    # Counterbalance: about half start in Low German  [§5.1]
    for participant in participants:
        order = order_for(participant, variants)
        # Same topic, content and course in both variants  [§5.1]
        for variant in order:
            dialogue(robot, participant, variant)
            # 18 RoSAS items, 5-point scale, after each dialogue  [§5.1]
            ratings[participant, variant] = rosas(participant)
    return ratings

# Factor score: sum of its six items, range 6-30  [§5.1]
def factor_score(ratings, items):
    return sum(ratings[item] for item in items)

# Compare the two variants for one factor with a t-test, alpha 0.05  [§5.2]
def compare_factor(scores_high, scores_low):
    # The paper does not state whether a paired or unpaired test was used
    return t_test(scores_high, scores_low)

No equations or algorithms beyond these scores are given in the paper §5.

Hyperparameters

Results

Author-reported. Setup: 17 participants, aged 19 to 61, within-subject, Pepper robot, RoSAS on a 5-point scale, t-test at 0.05 §5, §5.1, §5.2. The authors describe perceived warmth and competence as generally high and perceived discomfort as generally low §5.2.

Figures 4 to 6 show each factor with error bars for standard deviation; Figure 3 shows the three factors together fig. 3 fig. 4 fig. 5 fig. 6. Factor means are not reported. The item means are in the table below.

Paper vs code

No official code. The paper names no code repository or project page §1 to §6, and the arXiv metadata carries no code link. A GitHub repository search for "Low German Pepper robot" and one for "RoSAS social robot" returned no results.

Pitfalls

• Statistical reporting is thin. The paper names a t-test but not whether it is paired or unpaired, although the design is within-subject §5.1 §5.2. It reports p-values only: no test statistics, degrees of freedom, effect sizes or confidence intervals §5.2.
• Three factors, no correction. The paper tests warmth, competence and discomfort separately and does not mention a correction for multiple comparisons §5.2.
• Discomfort is not significant. p = 0.056 is above the stated 0.05 level. The authors still read it as an indication of an effect §5.2. Treat it as a non-significant trend.
• Warmth is the only clear effect. Competence shows hardly any difference §5.2, and this is the main contrast with the cited prior work, which reported a possible drop in competence for non-standard speech §5.3.
• Speculation in the discussion. The explanations for the warmth effect (Low German as a language of childhood or grandparents, a cosy association) are conjectures. The study did not test them §5.3.
• Familiarity is not analysed. Self-rated knowledge of Low German splits the sample into 7 and 10 participants §5.1, and the discussion draws on it qualitatively §5.3. No result is reported separately by familiarity.
• Small, regional sample. The 17 participants all live in northern Germany §5.1. The effect may not transfer to other regions or languages.
• Language asymmetry. Participants always spoke High German, because the robot cannot recognise Low German. The study therefore covers robot speech only, not a bilingual conversation §5.1. The authors name recognising Low German as future work §6.
• Voice and text are underspecified. The Low German speech comes from Pepper's German synthetic voice, and the authors call it imperfect §4. The paper does not say how the Low German wording was produced or checked §4 §5.1.
• Scale details are missing. Only the endpoint labels of the 5-point scale are given §5.1. The questionnaire language is not stated, though the quoted question is in English §5.1.
• Real robot, not a video. The authors argue that a physical robot with a synthetic voice has a stronger effect than virtual robots or recorded video §5.3. Replications with a different robot or a recorded voice would test this.

Lineage

• Builds on: none stated.
• Related work the paper discusses: Lugrin et al. (RO-MAN 2020) on regional speech variants and Pazylbekov et al. (HRI 2019) on dialect and similarity attraction §3. Both are cited by name only; no card exists for them.
• Compared against: the standard-language version of the same robot dialogue §5.1.
• Superseded by: not known.