DiPCo -- Dinner Party Corpus
This provides a public benchmarking dataset for researchers working on noise-robust and distant speech processing, though it's an incremental contribution as a new dataset rather than a methodological advance.
The researchers created the DiPCo corpus, a speech dataset simulating dinner party conversations in home environments to address noise robustness and distant speech processing challenges. The dataset contains 10 sessions of 15-45 minutes each with audio recordings from multiple microphone setups and human-labeled transcripts.
We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment. The corpus was created by recording multiple groups of four Amazon employee volunteers having a natural conversation in English around a dining table. The participants were recorded by a single-channel close-talk microphone and by five far-field 7-microphone array devices positioned at different locations in the recording room. The dataset contains the audio recordings and human labeled transcripts of a total of 10 sessions with a duration between 15 and 45 minutes. The corpus was created to advance in the field of noise robust and distant speech processing and is intended to serve as a public research and benchmarking data set.