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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.

The design and operation of a vehicle’s heating, ventilation, and air conditioning (HVAC) system has great impact on the performance of the vehicle’s Automatic Speech Recognition (ASR) and Hands-Free Communication (HFC) system. HVAC noise provides high amplitudes of broadband frequency content that affects the signal to noise ratio (SNR) within the vehicle cabin, and works to mask the user’s speech. But what’s less obvious is that when the airflow from the panel vents or defroster openings can be directed toward the vehicle microphone, a mechanical “buffeting” phenomenon occurs on the microphone’s diaphragm that distresses the ASR system beyond its ability to interpret the user’s voice. The airflow velocity can be strong enough that a simple windscreen on the microphone is not enough to eliminate the problem. Minimizing this buffeting effect is a vital key to building a vehicle that meets the customer’s expectations for ASR and HFC performance.

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.

This paper describes two case studies in which multiple microphone processing (beamforming) and microphone location were evaluated to determine their impact on improving embedded automatic speech recognition (ASR) in a vehicle hands-free environment. While each of these case studies was performed using slightly different evaluation set-ups, some specific and general conclusions can be drawn to help guide engineers in selecting the proper microphone location and configuration in a vehicle for the improvement of ASR. There were some outcomes that were common to both dual microphone solutions. When considering both solutions, neither was equally effective across all background noise sources. Both systems appear to be far more effective for noise conditions in which higher frequency energy is present, such as that due to high levels of wind noise and/or HVAC (heating, ventilation and air conditioning) blower noise.

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 prerecorded 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. 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. Additionally, a Signal to Noise Ratio (SNR) experiment was conducted highlighting the inverse relationship with speech recognition performance.