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Gregory Koushnir

Publications and source records attributed to Gregory Koushnir.

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Gender Gap Analysis in News and Talk Online Radio Broadcast

Radio broadcasting remains a dominant medium of communication, reaching 82% of Americans ages 12 and older weekly. Given its broad media impact, gender representation on radio news and talk stations may play an important role in shaping social and cultural perceptions. In this study, we examined patterns of gender representation in radio broadcasts, focusing on gender-based differences in total speaking time, air-time allocation across the day, and participation across broadcast topics. The dataset comprises filtered recordings from 74 US news and talk radio stations, collected over a 24-hour period and yielding more than 1,400 hours of content. We analyzed the data using VANPY, an in-house voice-analysis framework that combines multi-channel radio recording with AI-based speaker diarization, gender classification, speech-to-text transcription, and topic analysis. The results revealed consistent gender differences in allocated broadcast time, with male speakers accounting for 77% of total speaking time (SD = 6.8%). This gap remained evident during commute periods, when female speaking time was approximately 7.5-9.5 minutes per hour, compared with approximately 30.6-32.9 minutes for male speakers. Topic analysis further showed male dominance across all examined content categories. Female representation was lowest in "Talk Show Segments", where women accounted for only 10.8% of speaking time. Even in "Entertainment News", where female representation was highest, women accounted for only 35.8% of speaking time. Beyond these findings on gender dynamics in broadcast media, VANPY is publicly available and provides a systematic approach for analyzing large-scale audio data. It can be applied not only to radio broadcasts, but also to other audio domains such as human-computer interaction, corporate communication, and security applications.

cs.CY

VANPY: Voice Analysis Framework

Voice data is increasingly being used in modern digital communications, yet there is still a lack of comprehensive tools for automated voice analysis and characterization. To this end, we developed the VANPY (Voice Analysis in Python) framework for automated pre-processing, feature extraction, and classification of voice data. The VANPY is an open-source end-to-end comprehensive framework that was developed for the purpose of speaker characterization from voice data. The framework is designed with extensibility in mind, allowing for easy integration of new components and adaptation to various voice analysis applications. It currently incorporates over fifteen voice analysis components - including music/speech separation, voice activity detection, speaker embedding, vocal feature extraction, and various classification models. Four of the VANPY's components were developed in-house and integrated into the framework to extend its speaker characterization capabilities: gender classification, emotion classification, age regression, and height regression. The models demonstrate robust performance across various datasets, although not surpassing state-of-the-art performance. As a proof of concept, we demonstrate the framework's ability to extract speaker characteristics on a use-case challenge of analyzing character voices from the movie "Pulp Fiction." The results illustrate the framework's capability to extract multiple speaker characteristics, including gender, age, height, emotion type, and emotion intensity measured across three dimensions: arousal, dominance, and valence.

cs.SD