Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?
This work addresses the challenge of speaker attribution in speech transcripts for applications like forensic analysis or conversational AI, but it is incremental as it adapts existing methods to a new domain with controlled benchmarks.
The paper tackles the problem of applying authorship verification models to transcribed speech, finding that while existing models perform well in some settings, their performance degrades significantly when conversational topics are controlled, with specific performance drops noted in controlled conditions.
Authorship verification is the task of determining if two distinct writing samples share the same author and is typically concerned with the attribution of written text. In this paper, we explore the attribution of transcribed speech, which poses novel challenges. The main challenge is that many stylistic features, such as punctuation and capitalization, are not informative in this setting. On the other hand, transcribed speech exhibits other patterns, such as filler words and backchannels (e.g., 'um', 'uh-huh'), which may be characteristic of different speakers. We propose a new benchmark for speaker attribution focused on human-transcribed conversational speech transcripts. To limit spurious associations of speakers with topic, we employ both conversation prompts and speakers participating in the same conversation to construct verification trials of varying difficulties. We establish the state of the art on this new benchmark by comparing a suite of neural and non-neural baselines, finding that although written text attribution models achieve surprisingly good performance in certain settings, they perform markedly worse as conversational topic is increasingly controlled. We present analyses of the impact of transcription style on performance as well as the ability of fine-tuning on speech transcripts to improve performance.