Positioning oneself in different roles: Structural and lexical measures of power relations between speakers in Map Task Corpus

Silber-Varod, Vered; Malayev, Sarit; Lerner, Anat

doi:10.1016/j.specom.2020.01.002

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…Typically, the domain is given, so the set of available roles is known in advance and Speaker Role Recognition (SRR) is viewed as a supervised classification task [36,37]. Even though various modalities have been explored in the literature [38][39][40], the linguistic variability between the speaker roles usually provides the most important cues for the task in hand [11].…”

Section: Speaker Role Recognitionmentioning

confidence: 99%

Linguistically Aided Speaker Diarization Using Speaker Role Information

Flemotomos,

Georgiou,

Narayanan

2019

Preprint

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Speaker diarization relies on the assumption that speech segments corresponding to a particular speaker are concentrated in a specific region of the speaker space; a region which represents that speaker's identity. These identities are not known a priori, so a clustering algorithm is typically employed, which is traditionally based solely on audio. Under noisy conditions, however, such an approach poses the risk of generating unreliable speaker clusters. In this work we aim to utilize linguistic information as a supplemental modality to identify the various speakers in a more robust way. We are focused on conversational scenarios where the speakers assume distinct roles and are expected to follow different linguistic patterns. This distinct linguistic variability can be exploited to help us construct the speaker identities. That way, we are able to boost the diarization performance by converting the clustering task to a classification one. The proposed method is applied in real-world dyadic psychotherapy interactions between a provider and a patient and demonstrated to show improved results.

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