Towards Human Parity in Conversational Speech Recognition
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TASLP.2017.2756440, IEEE/ACM Transactions on Audio, Speech, and Language Processing 1 Towards Human Parity in Conversational Speech Recognition Wayne Xiong, Member, IEEE, Jasha Droppo, Senior Member, IEEE, Xuedong Huang, Fellow, IEEE, Frank Seide, Member, IEEE, Mike Seltzer, Senior Member, IEEE, Andreas Stolcke, Fellow, IEEE, Dong Yu, Senior Member, IEEE, Geoffrey Zweig, Fellow, IEEE Abstract—Conversational speech recognition has served as is especially difficult due to the spontaneous (neither read a flagship speech recognition task since the release of the nor planned) nature of the speech, its informality, and the Switchboard corpus in the 1990s. In this paper, we measure a self-corrections, hesitations and other disfluencies that are human error rate on the widely used NIST 2000 test set for commercial bulk transcription. The error rate of professional pervasive. The Switchboard [10] and later Fisher [11] data transcribers is 5.9% for the Switchboard portion of the data, in collections of the 1990s and early 2000s provide what is to which newly acquainted pairs of people discuss an assigned topic, date the largest and best studied of the conversational corpora. and 11.3% for the CallHome portion where friends and family The history of work in this area includes key contributions by members have open-ended conversations. In both cases, our institutions such as IBM [12], BBN [13], SRI [14], AT&T automated system edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively.
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