In-Vehicle speech recognition robustness is a challenge to the automotive industry. Factors such as background noise level and microphone placement affect how well the system performs. Live hardware validation using on-road testing with subjects of varying accent and cultural background can be both costly and time consuming. In addition, it is arguably impractical to collect a statistically significant amount of data to draw proper conclusions on the results. This paper describes a method to validate in-vehicle speech recognition by combining synthetically mixed speech and noise samples with batch speech recognition. Vehicle cabin noises are pre-recorded along with the impulse response from the driver's mouth location to the cabin microphone location. These signals are combined with a catalog of speech utterances to generate a noisy speech corpus. Speech scaling to simulate the Lombard effect of raising ones voice in a noisy environment is a critical piece. After the initial investment of purchasing a large corpus of clean speech utterances, validation becomes a simple matter of combining noise with speech and running recognition offline on a computer. Running batch speech recognition can not only validate the recognition robustness of a particular vehicle model, but a greater understanding of the various factors that affect speech recognition can be achieved using this simulation technique via custom design of experiments. Several factors were examined to measure their relative importance on speech recognition robustness. These include road surface and vehicle speed, climate control blower noise, and driver's seat position. A summary of the main effects from these experiments are provided with the most significant factors coming from climate control noise.