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In this paper, a systems engineering approach is explored to evaluate the effect of design parameters that contribute to the performance of the embedded Automatic Speech Recognition (ASR) engine in a vehicle. This includes vehicle designs that influence the presence of environmental and HVAC noise, microphone placement strategy, seat position, and cabin material and geometry. Interactions can be analyzed between these factors and dominant influencers identified. Relationships can then be established between ASR engine performance and attribute performance metrics that quantify the link between the two. This helps aid proper target setting and hardware selection to meet the customer satisfaction goals for both teams.

The performance of a vehicle’s Automatic Speech Recognition (ASR) system is dependent on the signal to noise ratio (SNR) in the cabin at the time a user voices their command. HVAC noise and environmental noise in particular (like road and wind noise), provide high amplitudes of broadband frequency content that lower the SNR within the vehicle cabin, and work to mask the user’s speech. Managing this noise is a vital key to building a vehicle that meets the customer’s expectations for ASR performance. However, a speech recognition engineer is not likely to be the same person responsible for designing the tires, suspension, air ducts and vents, sound package and exterior body shape that define the amount of noise present in the cabin. If objective relationships are drawn between the vehicle level performance of the ASR system, and the vehicle or system level performance of the individual noise, vibration and harshness (NVH) attributes, a partnership between the groups is brokered.

As automakers begin to design the autonomous vehicle (AV) for the first time, they must reconsider customer interaction with the Automatic Speech Recognition (ASR) system carried over from the traditional vehicle. Within an AV, the voice-to-ASR system needs to be capable of serving a customer located in any seat of the car. These shifts in focus require changes to the microphone selection and placement to serve the entire vehicle. Further complicating the scenario are new sources of noise that are specific to the AV that enable autonomous operation. Hardware mounted on the roof that are used to support cameras and LIDAR sensors, and mechanisms meant to keep that hardware clean and functioning, add even further noise contamination that can pollute the voice interaction. In this paper, we discuss the ramifications of picking up the intended customer’s voice when they are no longer bound to the traditional front left “driver’s” seat.