Speaker diarization (answering 'who spoke when') is a widely researched subject within speech technology. Numerous experiments have been run on datasets built from broadcast news, meeting data, and call centers-the task sometimes appears close to being solved. Much less work has begun to tackle the hardest diarization task of all: spontaneous conversations in real-world settings. Such diarization would be particularly useful for studies of language acquisition, where researchers investigate the speech children produce and hear in their daily lives. In this paper, we study audio gathered with a recorder worn by small children as they went about their normal days. As a result, each child was exposed to different acoustic environments with a multitude of background noises and a varying number of adults and peers. The inconsistency of speech and noise within and across samples poses a challenging task for speaker diarization systems, which we tackled via retraining and data augmentation techniques. We further studied sources of structured variation across raw audio files, including the impact of speaker type distribution, proportion of speech from children, and child age on diarization performance. We discuss the extent to which these findings might generalize to other samples of speech in the wild.
The automatic analysis of conversational audio remains difficult, in part, due to the presence of multiple talkers speaking in turns, often with significant intonation variations and overlapping speech. The majority of prior work on psychoacoustic speech analysis and system design has focused on single-talker speech or multi-talker speech with overlapping talkers (for example, the cocktail party effect). There has been much less focus on how listeners detect a change in talker or in probing the acoustic features significant in characterizing a talker's voice in conversational speech. This study examines human talker change detection (TCD) in multi-party speech utterances using a behavioral paradigm in which listeners indicate the moment of perceived talker change. Human reaction times in this task can be well-estimated by a model of the acoustic feature distance among speech segments before and after a change in talker, with estimation improving for models incorporating longer durations of speech prior to a talker change. Further, human performance is superior to several online and offline state-of-the-art machine TCD systems.
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