Turn-taking is a basic organizational feature of human conversation and remains difficult to model in natural, synchronous dialog systems. While existing research has explored multimodal approaches and large language models for turn-ending prediction, there is a lack of naturalistic conversational corpora specifically addressing turn-taking dynamics in Turkish. This study introduces a multimodal Turkish conversational dataset of unscripted dyadic interactions, comprising synchronized front-facing video, per-speaker audio channels that allow overlapping speech to be attributed to individual speakers, and time-aligned transcriptions. Turn-taking prediction is formulated as a binary classification problem, and a Genetic Algorithm (GA) is employed to optimize interpretable decision rules derived from visual, acoustic, and linguistic features. A hybrid AND-OR rule representation is adopted in the proposed framework to represent the alternative cue combinations that precede a turn transition.
Figures & tables
Speaker Set
Dur. (min)
Speech (min)
Non-sp. (min)
Non-sp. (%)
# Words
user_01 & user_02
26.5
22.7
3.7
14.1
3,267
user_03 & user_04
16.0
13.7
2.3
14.4
2,236
user_01 & user_05
16.4
13.6
2.8
17.2
2,219
user_01 & user_04
24.4
20.9
3.5
14.5
3,290
user_03 & user_06
17.1
14.1
3.1
17.9
2,252
user_03 & user_07
25.5
22.9
2.6
10.1
3,856
TABLE I: Statistics by Conversation
Fig. 1: Turn labelling flow.
Speaker
# Words
% Words
Speak. (min)
% Speak. time
# Conv.
user_01
9,523
26.7
66.2
28.6
5
user_02
1,044
2.9
8.2
3.5
1
user_03
12,172
34.1
72.9
31.5
6
user_04
1,937
5.4
12.8
5.5
2
user_05
1,196
3.3
7.7
3.3
1
user_06
1,376
3.9
9.8
4.2
2
TABLE II: Speaker Contribution Statistics
Feature
Unit (range)
Visual (9), MediaPipe Face Mesh + Py-Feat, 16 fps
landmark_movement
norm. coord. (0–100)
au_intensity_change
AU intensity (0–1)
blink_rate
blinks per 2 s (0–10)
gaze_changes
normalized (0–1)
mouth_opening_change
MAR (0–50)
TABLE III: Feature Details
Fig. 2: Simplified chromosome representation containing two features for visualization purposes.
Parameter
Value
Population size / max. generations
500 / 900
Selection
tournament, size 5
Crossover
single-point on conditions, rate 0.85
Mutation
rate 0.10 per offspring, 1/3 each for
threshold, feature, logic operators
Elitism
top 5% carried over unchanged
TABLE IV: Genetic Algorithm Configuration
Generations
Precision
Recall
F1
0
32.4
49.3
39.1
100
38.6
60.4
47.1
300
45.1
66.0
53.6
500
45.9
71.2
55.8
700
46.0
73.6
56.6
900
46.1
74.6
57.0
TABLE V: Evolution of Model Performance Across Generations (5-fold averages)