Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants
This repository contains the instructions relative to the SVC Bias Assessment Dataset and associated methodology to uncover demographic bias in Voice Assistants. More details can be found in the following publication accepted to LREC-COLING 2024:
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants, LREC-COLING 2024
by Chloé Sekkat, Fanny Leroy, Salima Mdhaffar, Blake Perry Smith, Yannick Estève, Joseph Dureau and Alice Coucke.
If you use this code and/or the provided dataset in your research, please cite:
@inproceedings{svc-demographic-bias-assessment,
title = {Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants},
author = {Chloé Sekkat, Fanny Leroy, Salima Mdhaffar, Blake Perry Smith, Yannick Estève, Joseph Dureau and Alice Coucke},
journal = {Proceedings of the Conference on Language Resources and Evaluation and the In,ternational Conference on Computational Linguistics (2024)},
month = {March},
year = {2024}
}
- Installation of the repository
- Dataset access & download
- Repository organization
- Dataset manipulation
- NLU coverage: transcripts & labels
- Adding ASR model decodings
- Oracle JointBERT model
- NLU evaluation
- Demographic bias assessment
- License
The code was implemented with python 3.9. To install, create a virtual environment and add run to install requirements:
pip install -r requirements.txtIn order to access the dataset, please fill the following form. Upon completion, you will be granted access shortly and will be provided with a temporary url to download it.
To ease the use of this repository, we recommend users to add the un-tared dataset in the data folder, under the name
svc-dataset.
Below you can find a description of the repository organization:
svc-demographic-bias-assessment
├── .gitignore
├── LICENSE
├── requirements.txt
├── svc_demographic_bias_assessment # Source code, main package
├── data
├── w2v_predictions
└── all_w2v_test_predictions.json
├── bert_predictions
└── all_bert_predictions.json
├── plots
└── dataset # To be added after download
├── main_confusion_bias.py
├── main_dataset_manipulation.py
├── main_demographic_bias_assessment.py
├── main_descriptive_analysis.py
├── main_nlu_coverage.py
├── main_nlu_evaluation.py
└── README.md
For instance, if you add these two lines to the script:
random_examples = dataset_manipulator.get_k_random_examples(k=2)
pprint(random_examples)You will get 2 random examples such as:
{'audio_3yQOcaylN6QCjAK5': {'audioLengthMs': 4130,
'content': {'domain': 'Music',
'subdomain': 'TransportControl'},
'speaker': {'age': '66',
'age_group': '55-100',
'dialectal_region': 'USA Mid-Atlantic',
'ethnicity': None,
'gender': 'male',
'user_group': '6',
'user_id': '28081'},
'transcript': {'label': {'intent': 'VolumeDown',
'slots': [{'entity': {'name': 'container_type_ent'},
'name': 'container_type',
'range': {'end': 28,
'start': 23},
'value': {'normalized_slot_value': 'radio',
'slot_value': 'radio'}},
{'entity': {'name': 'volume_shift_subj_ent'},
'name': 'volume_shift_subj',
'range': {'end': 46,
'start': 34},
'value': {'normalized_slot_value': 'a '
'few '
'levels',
'slot_value': 'a '
'few '
'levels'}}]},
'normalized_text': 'turn the volume '
'of the radio '
'down a few '
'levels',
'text': 'turn the volume of the '
'radio down a few levels'}},
'audio_5QH6UPITaQKhrCPj': {'audioLengthMs': 2399,
'content': {'domain': 'Music',
'subdomain': 'TransportControl'},
'speaker': {'age': '23',
'age_group': '17-28',
'dialectal_region': 'USA Mid-Atlantic',
'ethnicity': None,
'gender': 'female',
'user_group': '6',
'user_id': '21004'},
'transcript': {'label': {'intent': 'Mute',
'slots': []},
'normalized_text': 'volume off',
'text': 'volume off'}}}
To manipulate the SVC Bias Assessment Dataset, we created a python class DatasetManipulator that can be accessed through
the main_dataset_manipulation.py script. The DatasetManipulator contains several methods that one can play with to get
information on the released dataset.
All available methods are:
[
'display_transcript_by_audio_id',
'get_all_audio_ids_associated_to_given_intent',
'get_all_transcripts_associated_to_given_intent',
'get_k_random_examples',
'get_list_of_intents_in_dataset',
'get_music_entities_coverage',
'get_number_of_audios',
'get_number_of_audios_per_intent_type',
'get_number_of_hours',
'get_number_of_hours_per_intent_type',
'get_number_of_unique_speakers',
'get_number_of_unique_transcripts',
'get_number_of_unique_transcripts_per_intent_type',
'get_slot_name_coverage',
'play_audio_file_by_audio_id',
'play_audio_files_speaker_id'
]
To get an overview of the NLU coverage provided in the release dataset, you can run the following command:
python3 main_nlu_coverage.py \
--train_metadata_filepath ./data/svc_dataset/metadata/train_metadata.json \
--save_data_directory ./dataNote that all results given by this script use only the unique number of utterance (i.e. all duplicates are removed). This makes it easier to account for the large variety of music entities the released dataset provide.
Below is the count of slot_tuples among the unique utterances in the train split for PlayMusic. We can see that there
are 725 unique utterances containing only a song_name as slot while 333 contain both album_name and artist_name.
Here is the number of unique utterances per intent in the train split for TransportControl domain. For intsnace, there
are 80 unique utterances (i.e. different utterances) that belong to the Play intent.
Once you've chosen an ASR model and run the inference for each audio in the test set, you can save the predictions in the following format:
{
"audio_id": "prediction"
}You can refer to the examples given in data/w2v_predictions/all_w2v_test_predictions.json.
You can use any implementation of the JointBERT model and fine-tune it on the training set
of the proposed dataset (i.e. on all normalized queries, normalized_text field).
Once the model has been trained, it can be run on the ASR predictions for each audio in the test set. The model we used
led to the NLU predictions saved at data/bert_predictions/all_bert_predictions.json.
Once the previous steps have been done, we can perform the NLU evaluation, giving for each utterance the associated Exact Match metric.
python3 main_nlu_evaluation.py \
--bert_predictions_filepath ./data/bert_predictions/all_bert_predictions.json \
--test_metadata_filepath ./data/svc_dataset/metadata/test_metadata.json \
--save_data_directory ./data/bert_predictionsThis script will create the following file metadata_test_with_bert_predictions.json contains both the original metadata
and the SLU predictions.
This is the core of this repository.
This script allows to generate several descriptive statistics and figures provided in the paper.
python3 main_descriptive_analysis.py \
--set_ test \
--metadata_filepath ./data/svc_dataset/metadata/test_metadata.json \
--save_data_directory ./data \
--save_figures This command given above will lead to the following figures:
This script allows to generate the confusion bias tables provided in the paper.
python3 main_confusion_bias.py \
--set_ test \
--metadata_filepath ./data/svc_dataset/metadata/test_metadata.json \
--save_data_directory ./dataFor instance, with the command given above, you'll get, amongst others, the percentage of audio samples by gender per dialectal region for the test split:
Distribution of gender per dialectal region:
+----------------------------+------------+--------------+
| | audio_id | percentage |
|----------------------------+------------+--------------|
| ('Asian', 'female') | 3229 | 62 |
| ('Asian', 'male') | 1999 | 38 |
| ('Inland-North', 'female') | 6468 | 52 |
| ('Inland-North', 'male') | 5967 | 48 |
| ('LatinX', 'female') | 6169 | 66 |
| ('LatinX', 'male') | 3239 | 34 |
| ('Mid-Atlantic', 'female') | 10537 | 57 |
| ('Mid-Atlantic', 'male') | 8013 | 43 |
| ('Midland', 'female') | 7927 | 63 |
| ('Midland', 'male') | 4627 | 37 |
| ('New England', 'female') | 4647 | 67 |
| ('New England', 'male') | 2287 | 33 |
| ('Southern', 'female') | 8495 | 55 |
| ('Southern', 'male') | 7051 | 45 |
| ('Western', 'female') | 8516 | 61 |
| ('Western', 'male') | 5333 | 39 |
+----------------------------+------------+--------------+This script is the core of the statistical methodology provided in the paper.
python3 main_demographic_bias_assessment.py \
--asr_predictions_filepath ./data/bert_predictions/metadata_test_with_bert_predictions.json \
--save_data_directory ./data \
--group_usa_dialectal_regions FalseResults show statistically significant differences in performance across age, dialectal region and ethnicity. Below is an example of the analysis of some of the performed tests:
The output of this univariate test is the following:
Univariate log reg for gender is statistically significant at the 5% level
Logistic regression pvalues:
Intercept 0.000000
C(gender)[T.male] 0.016558
Logit Regression Results
==============================================================================
Dep. Variable: exactlyParsed No. Observations: 94504
Model: Logit Df Residuals: 94502
Method: MLE Df Model: 1
Date: Tue, 05 Mar 2024 Pseudo R-squ.: 8.941e-05
Time: 16:56:57 Log-Likelihood: -32198.
converged: True LL-Null: -32201.
Covariance Type: nonrobust LLR p-value: 0.01641
=====================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------
Intercept 2.0984 0.014 154.891 0.000 2.072 2.125
C(gender)[T.male] 0.0515 0.021 2.396 0.017 0.009 0.094
=====================================================================================
------------------------------------------------
Odd ratios:
OR Lower CI Upper CI
Intercept 8.152853 7.939224 8.372229
C(gender)[T.male] 1.052819 1.009418 1.098086
------------------------------------------------
------------------------------------------------You can see that men are significantly better recognized than women since the pvalue is equal to
Univariate log reg for dialectal_region is statistically significant at the 5% level
Logistic regression pvalues:
Intercept 7.440429e-298
C(dialectal_region)[T.Inland-North] 2.410951e-158
C(dialectal_region)[T.LatinX] 6.799730e-04
C(dialectal_region)[T.Mid-Atlantic] 6.663805e-162
C(dialectal_region)[T.Midland] 7.670283e-127
C(dialectal_region)[T.New England] 1.380474e-89
C(dialectal_region)[T.Southern] 1.065570e-142
C(dialectal_region)[T.Western] 6.905810e-137
Logit Regression Results
==============================================================================
Dep. Variable: exactlyParsed No. Observations: 94504
Model: Logit Df Residuals: 94496
Method: MLE Df Model: 7
Date: Tue, 05 Mar 2024 Pseudo R-squ.: 0.02724
Time: 16:56:58 Log-Likelihood: -31324.
converged: True LL-Null: -32201.
Covariance Type: nonrobust LLR p-value: 0.000
=======================================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------------------------
Intercept 1.2142 0.033 36.887 0.000 1.150 1.279
C(dialectal_region)[T.Inland-North] 1.2589 0.047 26.811 0.000 1.167 1.351
C(dialectal_region)[T.LatinX] 0.1416 0.042 3.398 0.001 0.060 0.223
C(dialectal_region)[T.Mid-Atlantic] 1.1386 0.042 27.114 0.000 1.056 1.221
C(dialectal_region)[T.Midland] 1.0829 0.045 23.958 0.000 0.994 1.171
C(dialectal_region)[T.New England] 1.0597 0.053 20.069 0.000 0.956 1.163
C(dialectal_region)[T.Southern] 1.0995 0.043 25.434 0.000 1.015 1.184
C(dialectal_region)[T.Western] 1.1051 0.044 24.903 0.000 1.018 1.192
=======================================================================================================
------------------------------------------------
Odd ratios:
OR Lower CI Upper CI
Intercept 3.367586 3.157185 3.592008
C(dialectal_region)[T.Inland-North] 3.521621 3.211988 3.861101
C(dialectal_region)[T.LatinX] 1.152062 1.061724 1.250087
C(dialectal_region)[T.Mid-Atlantic] 3.122292 2.875614 3.390132
C(dialectal_region)[T.Midland] 2.953176 2.702809 3.226735
C(dialectal_region)[T.New England] 2.885496 2.601804 3.200122
C(dialectal_region)[T.Southern] 3.002811 2.758856 3.268338
C(dialectal_region)[T.Western] 3.019537 2.768006 3.293924
------------------------------------------------In this case, every group is better recognized than the Asian reference group. However, while requests from all American
regional groups have around 3 times more chance to be exactly parsed than Asians' (ORs are around
This step is crucial to shine light on possible mixed effects. Below is the raw output from our script:
INFO:__main__:Performing adjustment tests
Adjustment test with age_group, gender:
Test is statistically significant. But conclusions about age_group are unchanged at the 5% level therefore gender is NOT a confounding factor for age_group.
Test is statistically significant. But conclusions about gender are unchanged at the 5% level therefore age_group is NOT a confounding factor for gender.
Adjustment test with age_group, dialectal_region:
Test is statistically significant. But conclusions about age_group are unchanged at the 5% level therefore dialectal_region is NOT a confounding factor for age_group.
Test is statistically significant. But conclusions about dialectal_region are unchanged at the 5% level therefore age_group is NOT a confounding factor for dialectal_region.
Adjustment test with age_group, ethnicity:
Test is statistically significant. But conclusions about age_group are unchanged at the 5% level therefore ethnicity is NOT a confounding factor for age_group.
age_group is NOT a confounding factor for ethnicity at the 5% level
Adjustment test with gender, dialectal_region:
Test is statistically significant. dialectal_region is a confounding factor for gender. Conclusions about gender are changed at the 5% level
gender is NOT a confounding factor for dialectal_region at the 5% level
Adjustment test with gender, ethnicity:
Test is statistically significant. But conclusions about gender are unchanged at the 5% level therefore ethnicity is NOT a confounding factor for gender.
gender is NOT a confounding factor for ethnicity at the 5% level
Adjustment test with dialectal_region, ethnicity:
Test is statistically significant. But conclusions about dialectal_region are unchanged at the 5% level therefore ethnicity is NOT a confounding factor for dialectal_region.
dialectal_region is NOT a confounding factor for ethnicity at the 5% levelTo get more detail results, we encourage you to look deeper in the code to better grasp the logic and the complete computations.
TL;DR:
- dialectal region is a confounding factor for gender
- there is a cross-effect of age and dialectal region
This repository and the SVC Bias Assessment Dataset are under the following LICENSE. They can be used only for academic and/or research purposes, not for commercial use. Publication is permitted only if the dataset is unmodified and subject to the same license terms. Any publication must include a full citation of the paper:
Sekkat C. et al., 2024, "Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants"