The Rich Transcription 2007 Meeting Recognition Evaluation

By Jonathan G. Fiscus, Jerome Ajot, John S. Garofolo. 2007

143 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Speech & Audio

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jinyu Li
2. S. Renals
3. Pawel Swietojanski
4. Arnab Ghoshal
5. Takuya Yoshioka
6. Dong Yu
7. Naoyuki Kanda
8. Shinji Watanabe

In Evaluation & Benchmarks

1. Zhong Meng
2. S. Renals
3. Zhuo Chen
4. Naoyuki Kanda
5. Arnab Ghoshal
6. Pawel Swietojanski
7. Takuya Yoshioka
8. Yu Wu

In Speech & Audio

1. Pawel Swietojanski
2. S. Renals
3. Arnab Ghoshal
4. Takuya Yoshioka
5. Zhuo Chen
6. Zhong Meng
7. Naoyuki Kanda
8. Xiaofei Wang

Citations in the corpus

• Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone (2021, background): “Meeting transcription with a distant microphone has been widely studied as one of the most challenging problems for automatic speech recognition (ASR) [1, 2, 3].”
• Hybrid acoustic models for distant and multichannel large vocabulary speech recognition (2013, background): “However, there has been progress in distant speech recognition over the past decade, with a particular focus on the transcription of multiparty meetings captured using tabletop or wall-mounted microphones, stimulated by the NIST Rich Transcription (RT) evaluation campaigns [2].”
• Text Mining in Big Data Analytics (2020, background): “A key problem in speech recognition is that transcripts with high word error rate are obtained for documented speeches in poor audio conditions and spontaneous speech recorded in actual conditions, as pointed out by the NIST Rich Transcription Meeting program [71].”