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The work presented here is part of the research done in the field of voice biometrics. This paper helps to understand the state-of-the-art in speaker recognition technology potentially capable of solving challenges related to speaker identification (to identify a speaker among multiple speakers) and speaker verification/authentication (to recognize the current speaking person at a pre-defined access level and authenticate accordingly). The research was focused on performing an unbiased evaluation of two individual voice biometric services. The level of accuracy in identifying and authenticating individuals using these services provides an insight into the current state of technology and the state of what other dual authentication methods could be used to achieve a desired True Acceptance Rate (TAR) and False Acceptance Rates (FAR).

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.

Wheel Fight is the undesirable rotational response of a vehicle's steering wheel due to road input at any or all of the road/wheel tire patches. The type of road input that will cause wheel fight comes in two forms: continuous rough road surfaces such as broken concrete or transient inputs such as pot-holes and tar strips. An objective method to quantify a vehicle's wheel fight sensitivity would be of great value to the vehicle development engineer. To that end, a study was conducted on Ford's Vehicle Vibration Simulator (VVS) to gather subjective responses and use those as a basis for correlation to an objective metric. One road surface known to induce wheel fight consists of using a rubber strip and driving over it while impacting only one side of the vehicle. Under this condition, steering wheel data was acquired on five different light trucks from which paired comparison studies were conducted.

The steering wheel is one of the primary sensory inputs for vehicle vibration while driving. Past research on hand-arm vibration has focused on a hand gripping a rod or a hand on a flat plate. Little work has focused on the perception of vibration felt through an automotive steering wheel. This paper discusses the investigation conducted at Ford's Vehicle Vibration Simulator Lab to develop equal annoyance contours for hand-arm vibration. These contours were developed for four different degrees-of-freedom: vertical, lateral, longitudinal and rotation about the steering wheel center. Rotation about the steering wheel is commonly induced by a 1st order tire non-uniformity force and imbalance of the wheel/tire. These 1st order excitation forces generate vibration in the frequency range of 8-20 Hz.

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.

Customer annoyance of steady-state wind noise correlates well with loudness. A common objective metric to capture average loudness is the ISO532B or Zwicker method. However, it has been shown previously that time-varying wind noise can also significantly affect customer annoyance, independent of average loudness. Causes of time-varying wind noise include wind buffeting generated by other vehicles, and also wind gusting. This paper summarizes the development of an objective metric that correlates well with subjective impressions of wind gusting/buffeting. The model is based on a general impulsive noise model with parameters tuned specifically for time-varying wind characteristics. The model consists of a psychoacoustic processing stage followed by a gusting detection stage, where the psychoacoustic stage is extracted from a time-varying loudness model. The output of the gusting model is a time series that indicates the location and “intensity” of wind gusts.

The following document is a set of guidelines intended to be used as a reference for the practicing automotive sound quality (SQ) engineer with the potential for application to the field of general consumer product sound quality. Practicing automotive sound quality engineers are those individuals responsible for understanding and/or conducting the physical and perceptual measurement of automotive sound. This document draws upon the experience of the four authors and thus contains many “rules-of-thumb” which the authors have found to work well in their many automotive related sound quality projects over the past years. When necessary, more detailed publications are referenced. The intent of publication of this document is to provide a reference to assist in automotive sound quality work efforts and to solicit feedback from the general sound quality community as to the completeness of the material presented.