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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.

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.

Visual movement of automotive components can induce a sense of poor quality and/or reliability to the customer. Many times this motion is likely to induce squeaks and rattles that further degrade customer opinion. For both of these reasons, it may be necessary to quantify the visual motion of certain components. This paper deals with a study in which the angular displacement from the observer to a passenger-side seat back was correlated to the subjective impression of seat back motion. Minutes Of Arc (MOAs) were found to correlate well to the perception of 17 subjects who evaluated the seat back motion of a seat mounted to a TEAM Cube in which road vibrations were played into a passenger seat and subjects were instructed that the evaluation surface was a “rough road” surface. This was confirmed for both the driver observing the unoccupied passenger seat from the side and a rear seat passenger viewing the unoccupied front seat from behind.

This research is the result of an effort to objectively quantify idle vibration felt at the seat during steady-state idle conditions. A previously used seat vibration metric using the root-sum-square (RSS) of vertical, lateral and longitudinal degrees-of-freedom (DOFs) measured at the seat base was found to not adequately describe the human perception of 34 test subjects (R2=0.63). Using the Ford vehicle vibration simulator, a new metric was developed. Thirty-four test subjects participated in a paired comparison study in which six-DOF (vertical, lateral, longitudinal, pitch, roll and yaw) simulations were reproduced from eight different vehicles. The stimuli used in the study spanned a wide range of vehicles, engine types and configurations. The paired comparison subjective results were used in a correlation of objective metrics. The resulting metric takes vibration measured at various locations of the seat base and projects these vibrations to the seat top.

Transient road disturbances excite complex vehicle responses involving the interaction of suspension/chassis, powertrain, and body systems. Typical ones are due to the interactions between tires and road expansion joints, railway crossings and other road discontinuities. Such transient disturbances are generally perceived as “impact harshness” due to the harshness perception as sensed by drivers through both sound and vibration. This paper presents a study of quantifying the effects of sound, steering wheel and seat/floorpan vibrations on the overall perception of the “impact harshness” during impact transient events. The Vehicle Vibration Simulator (VVS) of the Ford Research Laboratory was used to conduct this study. The results of the study show that sound and vibration have approximately equal impact on the overall perception of impact harshness. There is no evidence of interaction between sound and vibration.

Hands-free phone use is the most utilized use case for vehicles equipped with infotainment systems with external microphones that support connection to phones and implement speech recognition. Critically then, achieving hands-free phone call quality in a vehicle is problematic due to the extremely noisy nature of the vehicle environment. Noise generated by wind, mechanical and structural, tire to road, passengers, engine/exhaust, HVAC air pressure and flow are all significant contributors and sources of noise. Other factors influencing the quality of the phone call include microphone placement, cabin acoustics, seat position of the talker, noise reduction of the hands-free system, etc. This paper describes the work done to develop procedures and metrics to quantify the effects that influence the hands-free phone call quality.