TY - JOUR

T1 - Collaboration with local fieldworkers to support remote collection of high quality audio speech data

AU - Almbark, Rana

AU - Hellmuth, Sam

AU - Brown, Georgina

PY - 2023/12/27

Y1 - 2023/12/27

N2 - In 2022 we planned speech data collection with speakers of Syrian and Jordanian dialects to inform an updated Syrian Arabic dialectology in response to sustained displacement of millions of Syrians. The pandemic imposed remote data collection, but an internet-based approach also facilitated recruitment with this highly distributed speech community. Their vulnerable situation brings barriers, however, since most prospective participants have limited internet data and rarely use email. We collected self-recorded short audio files in which participants read scripted materials and described pictures. Three platforms were tested: Gorilla, Phonic and Awesome Voice Recorder (AVR, smartphone app). Gorilla/Phonic offer stimulus presentation advantages, so were piloted thoroughly, but the audio quality obtained was not suitable for phonetic analysis, ruling out their use in the main study. AVR yields full spectrum wav files but requires participants to submit files by email, so we recruited local fieldworkers to support participants with recording and file submission. We asked fieldworkers and participants about their experience of working with us, through surveys and interviews. The results confirm fieldworker involvement was crucial to the success of the project which generated high quality audio data, suitable for phonetic analysis, from 134 speakers within three months (Almbark, Hellmuth, & Brown, forthcoming).

AB - In 2022 we planned speech data collection with speakers of Syrian and Jordanian dialects to inform an updated Syrian Arabic dialectology in response to sustained displacement of millions of Syrians. The pandemic imposed remote data collection, but an internet-based approach also facilitated recruitment with this highly distributed speech community. Their vulnerable situation brings barriers, however, since most prospective participants have limited internet data and rarely use email. We collected self-recorded short audio files in which participants read scripted materials and described pictures. Three platforms were tested: Gorilla, Phonic and Awesome Voice Recorder (AVR, smartphone app). Gorilla/Phonic offer stimulus presentation advantages, so were piloted thoroughly, but the audio quality obtained was not suitable for phonetic analysis, ruling out their use in the main study. AVR yields full spectrum wav files but requires participants to submit files by email, so we recruited local fieldworkers to support participants with recording and file submission. We asked fieldworkers and participants about their experience of working with us, through surveys and interviews. The results confirm fieldworker involvement was crucial to the success of the project which generated high quality audio data, suitable for phonetic analysis, from 134 speakers within three months (Almbark, Hellmuth, & Brown, forthcoming).

KW - Computer Science Applications

KW - Linguistics and Language

KW - Language and Linguistics

U2 - 10.16995/labphon.10541

DO - 10.16995/labphon.10541

M3 - Journal article

VL - 14

JO - Laboratory Phonology

JF - Laboratory Phonology

SN - 1868-6346

IS - 1

ER -