SPEECHDAT-CAR. A Large Speech Database for Automotive Environments
By A. Moreno, B. Lindberg, C. Draxler, Gaël Richard, Khalid Choukri, S. Euler, Jeffrey Allen. 2000
151 citations recorded by the source corpus.
languages are Danish, British English, Finnish, Flemish/Dutch, French, German, Greek, Italian, Spanish and American English. For each language 600 sessions will be recorded (from at least 300 speakers) in seven characteristic environments (low speed, high speed with audio equipment on, etc.). This paper gives an overview of the project with a focus on the production phases (recording platforms, speaker recruitment, annotation and distribution).
Subfields: Artificial Intelligence, Speech & Audio, Signal Processing
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
- Jinyu Li
- Yifan Gong
- Li Deng
- Dong Yu
- Reinhold Häb-Umbach
- Yoshua Bengio
- José C. Segura
- Antonio J. Rubio
In Speech & Audio
- Jinyu Li
- Yifan Gong
- Li Deng
- Reinhold Häb-Umbach
- José C. Segura
- Antonio J. Rubio
- Dong Yu
- Yoshua Bengio
In Signal Processing
Citations in the corpus
- CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, neutral): “This can be attributed to advances in speech processing, audio enhancement, and machine learning, but also to the availability of real speech corpora recorded in cars [7, 8], quiet indoor environments [9, 10], noisy indoor and outdoor environments [11, 12], and challenging broadcast media [13,14].”
- Efficient voice activity detection algorithms using long-term speech information (2004, methodology): “Recently, various voice activity detection procedures have been described in the literature for several applications including mobile communication services (Freeman et al., 1989), real-time speech transmission on the Internet Nomenclature AFE advanced front end AMR adaptive multi-rate DSR distribut”
- Deep Learning for Environmentally Robust Speech Recognition (2017, unknown): “(mainly) single Aurora-3 [49] TIDigits car real add.”
- An Overview of Noise-Robust Automatic Speech Recognition (2014, background): “The Aurora 3 task consists of noisy speech data recorded inside cars as part of the SpeechDatCar project [27].”