Speech recognition in the noisy car environment

Ruehl, H.W.; Dobler, Stefan; Weith, J.; Meyer, Peter; Noll, Andreas; Hamer, Hans-Hermann; Piotrowski, H.

doi:10.1016/0167-6393(91)90024-n

Cited by 18 publications

(1 citation statement)

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“…ASR: An overview of early methods for car noise compensation in ASR is provided in Ruehl et al (1991). For speech recognition, either matched models (models with comparable noise and noise type to input) can be used for decoding the observed noisy speech, or approaches to speech enhancement such as that proposed by Alexandre and Lockwood (1993) where root cepstral analysis is performed to obtain improvements in isolated word recognition, or Hansen and Clements (1991) which employ noise dependent Auto-LSP enhancement for large vocabulary speech recognition front-ends.…”

Section: Vad (Voice Activity Detection)mentioning

confidence: 99%

Car noise verification and applications

Krishnamurthy

Hansen

2013

Int J Speech Technol

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This study presents audio based vehicle-verification as a new area of research. The task involves verifying the claim that an acoustic sample belongs to a vehicle. Audio based vehicle verification has the potential to impact research in the areas of vehicle forensics and in-vehicle speech systems. For this task, a new corpus (UTD-CAR-NOISE) that consists of noise from 20 vehicles under 8 distinct noise environments (∼8 hours of data). Our approach towards vehicle verification hypothesizes that some specific environments are more suited for vehicle verification. Towards this goal, four diverse in-vehicle noise conditions are identified on the basis of their frequency of occurrence. Additionally, four different verification systems are proposed based on their complexity and modeling strategies. Our evaluation shows that A/C on with windows closed condition is the most conducive for vehicle verification (98 %). The proposed systems were evaluated on approximately 100,000 trials, achieving performances in the range of (75-98 %) for different vehicle environments.

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Section: Vad (Voice Activity Detection)mentioning

confidence: 99%