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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.

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.

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.

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.

Impact harshness characterizes interior sound and vibration resulting from tire interactions with discrete road disturbances. Typical interactions are expansion joints, railroad crossings, and other road discontinuities at low-to-medium vehicle speeds. One goal of the current study was to validate for light trucks and SUVs the metric that was developed for cars: a weighted combination of peak loudness values from the front and rear impacts after lowpass filtering at 1 kHz. Another goal was to see if other sound characteristics of impact harshness needed to be captured with a metric. A listening study was conducted with participants evaluating several different trucks and SUVs for impact harshness. Results show that the existing metric correlates well with subjective preferences for most of the vehicles.

Many engine tick and knock issues are clearly audible, yet cannot be characterized by common sound quality metrics such as time-varying loudness, sharpness, fluctuation strength, or roughness. This paper summarizes the recent development and application of an objective metric that agrees with subjective impressions of impulsive engine noise. The metric is based on a general impulsive noise model [1], consisting of a psychoacoustic processing stage followed by a transient detection stage. The psychoacoustic stage is extracted from portions of a time-varying loudness model. The primary output of the impulsive engine noise model is a time series that indicates the location and “intensity” of impulsive engine noise events. The information in this time series is reduced either to a single number metric, or to a frequency-based vector of numbers that indicates the amount of impulsiveness in the recorded sound.

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.

When driving or riding in a vehicle, the customer is bombarded with sensory stimuli. These include tactile, auditory, olfactory and visual. In addition, the customer may be asked to perform various routine driving tasks that can have an influence on the perception of each of the aforementioned senses. Or perhaps, the influence of one sense may affect the perception of another. Since sound rarely occurs void of felt vibration and vice-versa, there is reason to believe one may influence the perception of the other, or that the two may interact in some way when the customer is exposed to a particular NVH (Noise Vibration and Harshness) event in a vehicle. The NVH engineer wishes to gage a sound or vibration's impact on the customer and make a determination as to whether corrective actions on the vehicle are necessary. NVH issues routinely show up as top warranty and customer satisfaction concerns.

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.

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.

It is well known that customer perception of the annoyance of steady-state wind noise can be fairly well characterized by calculating the loudness of such sounds. Commonly used is the ISO532B or Zwicker method [1]. What is not known, however, is how a customer would react to time-varying wind noise. Such situations can occur when a vehicle experiences cross-wind conditions on the highway. Turbulent air flow generated by either a passing vehicle or when traveling in the wake of another vehicle can cause the wind noise to take on time-varying characteristics. The time-varying wind noise created by such situations is commonly referred to as “buffeting.” Customer complaint field data indicates that wind buffeting is a source of annoyance, but the level of the effect has never been quantified. In this study, binaural sounds were recorded inside an aeroacoustic wind tunnel. Varying degrees of buffeting were simulated using a “blocker” vehicle situated in front of the test vehicle.

Transient automotive sounds often possess a complex internal structure resulting from one or more impacts combined with mechanical and acoustic cavity resonances. This structure can be revealed by a new technique for obtaining translation-invariant scalograms from orthogonal discrete wavelet transforms. These scalograms are particularly well suited to the visualization of complex sound transients which span a wide dynamic range in time (ms to s) and frequency (∼100Hz to ∼10kHz). As examples, scalograms and spectrograms of door latch closing events from a variety of automotive platforms are discussed and compared in light of the subjective rankings of the sounds.

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, an approach to analyze voice recognition data to understand how customers use voice recognition systems is explored. The analysis will help identify ASR failures and usability related issues that customers encounter while using the voice recognition system. This paper also examines the impact of these failures on the individual speech domains (media control, phone, navigation, etc.). Such information can be used to improve the current voice recognition system and direct the design of future systems. Infotainment system logs, audio recordings of the voice interactions, their transcriptions and CAN bus data were identified to be rich sources of data to analyze voice recognition usage. Infotainment logs help understand how the system interpreted or responded to customer commands and at what confidence level.