An In-car Chinese Noise Corpus for Speech Recognition

In this paper, we present an in-car Chinese noise corpus that can be used in simulating complicated car environment for robust speech recognition research and experiment. The corpus was collected in mainland China in 2009 and 2010. The corpus includes a diversity of car conditions including different car speed, open/close windows, weather conditions as well as environment conditions. Specially, the rumble strips are also taken into account due to the typical noise generated as the car is passing on. In order to use the corpus efficiently, we performed some acoustic signal analyses on those noise data, mainly focused on stationary properties and energy distribution in the frequency domain. We also performed ASR experiments using selected noise data from the corpus, by adding noise data to clean speech to simulate the in-car environment. The corpus is the first of its kind for in-car Chinese noise corpus, providing abundant and diversified samples for car noise speech recognition task.