#  Executive Summary

A Data management plan for VOICI is described. Generally FAIR principles will
be followed (findable, accessible, interoperable and reusable data). The
consortium or the Topic Manager may, however, decide that individual data
items are too sensitive for FAIR to apply, if this is justified by privacy of
persons or commercial exploitation of results. Data will be deposited on the
Zenodo repository. The data will mainly be speech and cockpit noise recordings
obtained in the Audio Evaluation Environment (the VOICI lab).

**About the project:** The main objective of VOICI is to demonstrate the
technology that implements an intelligent natural crew assistant in a cockpit
environment up to TRL 3. This is implemented through the following _specific
objectives_ as stated in the DoA:

**Obj. 1:** Develop a non-intrusive voice acquisition system that allows
separating speakers from each other and filtering speakers from background
noise. The ambition is to acquire speech input of sufficient quality from both
(i) crew headsets and (ii) an ambient microphone array system.

**Obj. 2:** Develop an audio evaluation environment emulating the audio
environment in the cockpit.

**Obj. 3:** Develop a high-end speech recognizer that reaches a word error
rate (WER) of 5% in harsh environment. This engine will recognize not only
aircrew requests but also inputs from the ATC radio communication.

**Obj. 4:** Develop an intelligent agent that naturally interacts with the
aircrew and the existing aircraft system through pre-defined flight scenarios
and that is aware of the flight situation through communication with other
cockpit sub-systems and flight procedures  1  .

**Obj. 5:** Develop the whole system in such way that it can be ultimately
embarked in the cockpit without external dependencies with external cloud-
based services.

**Obj. 6:** Test and evaluate the performances of the system under several
flight phases with the defined highlevel requirements.

#  Introduction

##  Purpose and structure of the document

The document describes how the VOICI consortium aims to manage scientific data
within the project, with emphasis on data that will be openly accessible in
accordance with the Horizon 2020 Open Research Data Pilot. The report is based
on _H2020 templates: Data management plan v1.0 – 13.10.2016._

##  Intended readership

The document is mainly intended for Clean Sky 2, the Topic Manager and the
VOICI consortium, but is open to any interested reader.

#  Data Summary

Data are gathered to facilitate development of technology for

  * noise suppression for speech-input (WP1-2) 

  

  * speech recognition for targeted vocabulary in noisy environment (WP2) 

  

  * natural language understanding of operational requests and dialog management system (WP3) 

  

  * computer generated speech as output from dialog systems (WP3) 

  

A target cockpit has been selected by Thales as Topic Manager: a Dassault
Falcon 2000 business jet. To aid technology development and evaluation an
Audio Evaluation Environment (AEE) is established in WP1: a laboratory that
recreates the geometry and noise conditions of the target cockpit.

##  Pre existing data to be used

  1. LibriSpeech ASR corpus from  _http://www.openslr.org/12/_ to train an English speech recognition engine. 

  

  2. Falcon 2000 aircraft dimensions to build AEE (approximate from drawings and photos). (C) TTS Voice portfolio of Acapela Group 

  

  

##  Data anticipated to be obtained/generated in VOICI – and their planned
accessibility

For details about data already published see the tables in Appendix A.
Remaining items for publication in the below list will be reported in the same
manner.

  1. Cockpit noise recorded in real Falcon 2000 jets, effectively a single microphone per flight session. 

  

  

See Appendix A.2.

_Format_ : wav _Size_ : 0.1-1 gigabytes (GB) per recording

_Origin_ : Thales, SINTEF _Dissem. level_ : Public

_Publication_ : May 2019.

  2. Speech recorded in the Audio Evaluation Environment (AEE): Speech as defined in section 2 played back over a Head and Torso Simulator (HATS) and recorded via headset microphone 

  

  

_Format_ : wav + txt for transcription _Size_ : up to 150GB

_Origin_ :  _http://www.openslr.org/12/_ _Dissem.level_ : Public 0
_Publication anticipated_ : Dec 2019

  3. Speech recorded in the Audio Evaluation Environment (AEE): Speech played back over a Head and Torso Simulator (HATS) and recorded via microphone arrays 

  

  

_Format_ : wav + txt for transcription _Size_ : up to 150GB per microphone

_Origin_ :  _http://www.openslr.org/12/_ _Dissem.level_ : Public 0
_Publication anticipated_ : Dec 2019

  4. Noise in the AEE, based on (i) above, recreated using multiple distributed loudspeakers, and recorded by the same microphones as in (i) and (iii) above. 

  

  

_Format_ : wav _Size_ : up to 150GB per microphone

_Origin_ : Thales, SINTEF _Dissem.level_ : Public 0

_Publication anticipated_ : Dec 2019

  5. Impulse responses between different sound sources (speech, noise) and receivers (microphones). 

  

  

_Format_ : wav _Size_ per respons typically 0.1-1 megabytes (MB).

_Origin_ : SINTEF _Dissem.level_ : Public

_Publication anticipated_ : Dec 2019

  6. Recordings from a professional speakers of the voice talent to create the Assistant voice 

  

  

Format : wav Size : 2 GB

Origin : ACAPELA _Dissem. level_ ; Confidential

  7. Speech data recorded by pilots using operational requests (ATC communications and/or voice assistant requests). 

  

  

_Format_ : wav + txt for transcription _Size_ : X GB

_Origin_ : Thales _Dissem.level_ : Confidential (Commercial)

**Utility of data** : The purpose of all data is to facilitate technical
development within the project. All the above items are likely to be of
interest to the academic community carrying out research on microphone arrays,
speech recognition and dialog systems. In particular, interest is expected for
aviation applications.

Access to data is described in section 3.

#  FAIR data

VOICI aims to make project data findable, accessible, interoperable and
reusable (FAIR) in accordance with the guidelines of the Horizon 2020 Open
Research Data Pilot. The consortium or the Topic Manager may, however, decide
that individual data items are too sensitive for FAIR to apply, if this is
justified by privacy of persons or commercial exploitation of results. This
will be implemented as follows: The institution responsible for a data item
will notice the others by email when it is ready for publication and await
objections within four weeks. The data will then be promptly published unless
objections are received. In the latter case publication will be abandoned. The
responsible institution may then ask the Steering Committee to decide, in
dialog with the Topic Manager. The final decision will in this case be based
on the Consortium Agreement, i.e. mainly founded on consensus principles.

##  Making data findable, including provisions for metadata

VOICI will use th  e _Zenodo repository_ as the main tool to comply with H2020
Open Access mandate. A VOICI community will be established. All public data
sets and scientific articles/papers will be uploaded to this community in
Zenodo and enriched with standard Zenodo metadata, including Grant Number and
Project Acronym. Data sets that are not public will be stored at the relevant
partner Relevant keywords will be assigned to each data set.

Zenodo provides version control and assigns DOIs to all uploaded elements.

**Naming conventions:**

Data will be named using the following naming conventions:

_Descriptive text CleanSky2_VOICI_DeliverableNumber_UniqueDataNumber_

_Descriptive text CleanSky2_VOICI_PublicationNumber_UniqueDataNumber_

_Data set folder CleanSky2_VOICI_DatasetNumber_ UniqueDataNumber_

For each speaker, a unique folder will be created (for protection of speakers'
identity, see section 5) _CleanSky2_VOICI_DatasetUser_UniqueUserID_

**Digital Object Identifiers (DOI)**

DOI's for all datasets will be reserved and assigned with the DOI
functionality provided by Zenodo. DOI versioning will be used to assign unique
identifiers to updated versions of the data records.

**Metadata**

Metadata associated to each published dataset will by default be

  * Digital Object Identifiers and version numbers 

  

  * Bibliographic information 

  

  * Keywords 

  

  * Abstract/description 

  

  * Associated project and community 

  

  * Associated publications and reports 

  

  * Grant information 

  

  * Access and licensing info 

  

  * Language 

  

**Metadata** for speech corpus (TIMIT-like metadata)

  1. General presentation : template from default metadata (see above) 

  

  2. Corpus speaker distribution : native speaker (specify dialect regions) or not (specify mother language), male or female, number of speakers and their age, pilot or not. 

  

  3. Corpus text material : description of the sentences used by the speakers, distribution of sentences per speaker. 

  

  

##  Making data openly accessible

All data sets with dissemination level "Public" will be uploaded to Zenodo and
made open and free of charge.

Publications and underlying data sets will be linked through persistent
identificators, DOI.

Metadata including licences for individual data records as well as record
collections will be harvestable using the OAI-PHM protocol by the record
identifier and the collection name. Metadata is also retrievable through the
public REST API. The data will be available through www.zenodo.org, and hence
accessible using any web browsing application.

Data sets with dissemination level "Confidential" will not be shared due to
commercial exploitation and/or person privacy protection. Zenodo's mechanism
for time-limited embargo will be considered in individual cases.

##  Making data interoperable

Zenodo uses JSON Schema as internal representation of metadata and offers
export to other popular formats such as Dublin Core, MARCXML, BibTeX, CSL,
DataCite and export to Mendeley. The data record metadata will utilise the
vocabularies applied by Zenodo. For certain terms these refer to open,
external vocabularies, e.g.: license (Open Definition), funders (FundRef) and
grants (OpenAIRE). Reference to any external metadata is done with a
resolvable URL.

##  Increase data re-use (through clarifying licences)

VOICI will enable third parties to access, mine, exploit, reproduce and
disseminate (free of charge for any user) all public data sets, and regulate
this by using  _Creative Commons Licences._

As default, the CC-BY-SA license will be applied for public VOICI data. This
license lets others remix, tweak, and build upon published work even for
commercial purposes, as long as they credit the original work and license
their new creations under the identical terms. This license is often compared
to “copyleft” free and open source software licenses. All new/derived work
will carry the same license as the original, so any derivatives will also
allow commercial use. This does not preclude use of less restrictive licenses
as CC-BY or more restrictive licenses as CC BY-NC not allowing commercial
usage. This will be assessed in each case.

For data published in scientific journals the dataset will be made available
simultaneously with granting of open access for the paper or the preprint. The
data will be linked to the paper. For data associated with public deliverables
data will be shared after approval of the deliverable by the EC.

Open data will be reusable as defined by their licenses. Data classified as
confidential will as default not be reusable due to commercial exploitation or
privacy of persons.

The public data will remain re-usable for unlimited time only limited by the
lifetime of the Zenodo repository. This is currently the lifetime of the host
laboratory CERN, which currently has an experimental programme defined for the
next 20 years at least. In cases Zenodo is phased out their policy is to
transfer data/metadata to other appropriate repositories.

#  Allocation of resources

VOICI uses standard tools, and a free of charge repository. The costs of data
management activities are limited and will be covered by the project grants.
Potential resource needs to support reuse of data after the active project
period will be solved from case to case.

SINTEF is the lead for WP 4 Dissemination, communication and exploitation.

#  Data security

**Repository - data security is as specified by the Zenodo**

  1. Versions: Data files are versioned. Records are not versioned. The uploaded data is archived as a Submission Information Package. Derivatives of data files are generated, but original content is never modified. Records can be retracted from public view; however, the data files and record are preserved. 

  

  2. Replicas: All data files are stored in CERN Data Centres, primarily Geneva, with replicas in Budapest. Data files are kept in multiple replicas in a distributed file system, which is backed up to tape on a nightly basis. 

  

  3. Retention period: Items will be retained for the lifetime of the repository. This is currently the lifetime of the host laboratory CERN, which currently has an experimental programme defined for the next 20 years at least. 

  

  4. Functional preservation: Zenodo makes no promises of usability and understandability of deposited objects over time. 

  

  5. File preservation: Data files and metadata are backed up nightly and replicated into multiple copies in the online system. 

  

  6. Fixity and authenticity: All data files are stored along with a MD5 checksum of the file content. Files are regularly checked against their checksums to assure that file content remains constant. 

  

  7. Succession plans: In case of closure of the repository, best efforts will be made to integrate all content into suitable alternative institutional and/or subject based repositories. 

  

  

###  Use of recorded speech data

Data management procedures will protect the confidentiality of human speakers
taking part in the VOICI project. A unique subject number will be assigned to
each speaker immediately after informed consent has been obtained. This number
will serve as the speaker’s identifier in the validated database. The
speaker’s recorded speech will be stored under this number. Only the partners
will be able to link the speakers’ data to a specific subject via an
identification list kept private among the partners. Data protection and
privacy regulations will be observed in capturing, forwarding, processing, and
storing subjects’ data. Speakers will be informed accordingly and will be
requested to give their consent on data handling procedures in accordance with
national regulations and the EU General Data Protection Regulation (GDPR).

#  Ethical aspects

Other than what is mentioned above, under "recorded speech data", no ethical
or legal issues have been identified, that can have an impact on data sharing.

###  Appendix

#  A Data generated

A Zenodo community has been established: **(H2020 CleanSky JU) VOICI**
_https://www.zenodo.eu/communities/h2020_cleansky_voici_

The following data, A.1 and A.2, have been prepared and made available on this
community:

##  A.1 Clean Sky 2 VOICI project information  
  
<table>  
<tr>  
<th>

Name of IADP/ITD/TA/TE2/Domain

</th>  
<th>

SYSTEMS ITD

</th> </tr>  
<tr>  
<td>

Data Storage

</td>  
<td>

ZENODO

</td> </tr>  
<tr>  
<td>

Link to repository

</td>  
<td>

_https://zenodo.org/_

</td> </tr>  
<tr>  
<td>

Dataset Identifier

</td>  
<td>

</td> </tr>  
<tr>  
<td>

DOI number (10.5281/zenodo.2658911)

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

Relevant Keywords

</td>  
<td>

Cockpit, Noise, Crew assistant

</td> </tr>  
<tr>  
<td>

Data Licence

</td>  
<td>

Creative Commons Attribution-ShareAlike 4.0 International

</td> </tr>  
<tr>  
<td>

Date for Data Publication

</td>  
<td>

2019-05-03

</td> </tr>  
<tr>  
<td>

Date of data collection

</td>  
<td>

2019-05-03

</td> </tr>  
<tr>  
<td>

Data Version

</td>  
<td>

Zenodo DOI versioning

</td> </tr>  
<tr>  
<td>

Data Preservation time

</td>  
<td>

Lifetime of Zenodo

</td> </tr>  
<tr>  
<td>

Name of the Data Set Responsible (DSR)

</td>  
<td>

(owner of the data) Tor Arne Reinen

</td> </tr>  
<tr>  
<td>

DSR e-mail

</td>  
<td>

Tor.A.Reinen@sintef.no

</td> </tr>  
<tr>  
<td>

DSR Telephone

</td>  
<td>

+47 48288362

</td> </tr>  
<tr>  
<td>

Funding body(ies)

</td>  
<td>

European Union’s H2020 through Clean Sky 2 Programme.

</td> </tr>  
<tr>  
<td>

Grant number

</td>  
<td>

_785401_

</td> </tr>  
<tr>  
<td>

Partner organisations

</td>  
<td>

SINTEF, Multitel, sensiBel, Acapela

</td> </tr>  
<tr>  
<td>

Project duration

</td>  
<td>

Start: 2018-03-01 End: 2020-02-28

</td> </tr>  
<tr>  
<td>

Date DMP created

</td>  
<td>

2018-09-28

</td> </tr>  
<tr>  
<td>

Date last update

</td>  
<td>

2019-10-11

</td> </tr>  
<tr>  
<td>

Version

</td>  
<td>

No. 2

</td> </tr>  
<tr>  
<td>

Name of the DMPR (responsibilities for data management of the

IADP/ITD/TA/TE2)

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

DMPR e-mail

</td>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

DMPR Telephone

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

The main objective of VOICI is to

demonstrate the technology that implements an intelligent natural crew
assistant in a cockpit environment up to TRL 3.

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Description of the research

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data Collection

</td>  
<td>

</td>  
<td>

Description of the VOICI project, as background for other data items.

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Existence of similar data

</td>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Nature of the Data

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Text

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data Type

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

.pdf

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data Format

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

1.6 MB

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data Size

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

1

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Number of files

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Descriptive file

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

VOICI project info.pdf

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data File

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Quality/Accuracy

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Unit measurement system

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Universities, Research Centers

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Potential Users

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Ethical Issue

</td>  
<td>

</td> </tr> </table>

##  A.2 Cockpit noise Falcon 2000LXS TRH-OSL CleanSky2 VOICI Data 1  
  
<table>  
<tr>  
<th>

Name of IADP/ITD/TA/TE2/Domain

</th>  
<th>

SYSTEMS ITD

</th> </tr>  
<tr>  
<td>

Data Storage

</td>  
<td>

ZENODO

</td> </tr>  
<tr>  
<td>

Link to repository

</td>  
<td>

_https://zenodo.org/_

</td> </tr>  
<tr>  
<td>

Dataset Identifier

</td>  
<td>

</td> </tr>  
<tr>  
<td>

DOI number (10.5281/zenodo.2660112)

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

Relevant Keywords

</td>  
<td>

Cockpit, Noise, Crew assistant

</td> </tr>  
<tr>  
<td>

Data Licence

</td>  
<td>

Creative Commons Attribution-ShareAlike 4.0 International

</td> </tr>  
<tr>  
<td>

Date for Data Publication

</td>  
<td>

2019-05-03

</td> </tr>  
<tr>  
<td>

Date of data collection

</td>  
<td>

2018-09-13

</td> </tr>  
<tr>  
<td>

Data Version

</td>  
<td>

Zenodo DOI versioning

</td> </tr>  
<tr>  
<td>

Data Preservation time

</td>  
<td>

Lifetime of Zenodo

</td> </tr>  
<tr>  
<td>

Name of the Data Set Responsible (DSR)

</td>  
<td>

(owner of the data) Tor Arne Reinen

</td> </tr>  
<tr>  
<td>

DSR e-mail

</td>  
<td>

Tor.A.Reinen@sintef.no

</td> </tr>  
<tr>  
<td>

DSR Telephone

</td>  
<td>

+47 48288362

</td> </tr>  
<tr>  
<td>

Funding body(ies)

</td>  
<td>

European Union’s H2020 through Clean Sky 2 Programme.

</td> </tr>  
<tr>  
<td>

Grant number

</td>  
<td>

_785401_

</td> </tr>  
<tr>  
<td>

Partner organisations

</td>  
<td>

SINTEF, Multitel, sensiBel, Acapela

</td> </tr>  
<tr>  
<td>

Project duration

</td>  
<td>

Start: 2018-03-01 End: 2020-02-28

</td> </tr>  
<tr>  
<td>

Date DMP created

</td>  
<td>

2018-09-28

</td> </tr>  
<tr>  
<td>

Date last update

</td>  
<td>

2019-10-11

</td> </tr>  
<tr>  
<td>

Version

</td>  
<td>

No. 2: Year 1 review

</td> </tr>  
<tr>  
<td>

Name of the DMPR (responsibilities for data management of the

IADP/ITD/TA/TE2)

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

DMPR e-mail

</td>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

DMPR Telephone

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

The main objective of VOICI is to

demonstrate the technology that implements an intelligent natural crew
assistant in a cockpit environment up to TRL 3.

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Description of the research

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Cockpit noise recording, Falcon 2000 flight Trondheim – Oslo. Pilot speech and
pitchtrim signal removed.

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data Collection

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Existence of similar data

</td>  
<td>

</td>  
<td>

Not known

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Experimental data

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Nature of the Data

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Audio file

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data Type

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

.wav

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data Format

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

290 MB

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data Size

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

2 (data + descriptive file)

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Number of files

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Cockpit noise Falcon 2000LXS TRH-OSL description.pdf

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Descriptive file

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Data File

</td>  
<td>

</td>  
<td>

Recording

6_low_freq_boost_remove_speech_2_Wood

Pk_reinsert_16b.wav

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Quality/Accuracy

</td>  
<td>

</td>  
<td>

Calibrated recording, as specified in Descriptive file

</td> </tr>  
<tr>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Unit measurement system

</td>  
<td>

</td>  
<td>

SI

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Universities, Research Centers

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Potential Users

</td>  
<td>

</td> </tr>  
<tr>  
<td>

</td>  
<td>

NA

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Ethical Issue

</td>  
<td>

</td> </tr> </table>