On the feasibility of ASR in extreme noise using the PARAT earplug communication terminal

Strand, Ole Morten; Holter, Trym; Egeberg, A.; Stensby, S.

doi:10.1109/asru.2003.1318460

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…Heracleous et al (2003) used a stethoscope device to capture the bone vibrations of the head and used that for non-audible murmur recognition. Like in Strand et al (2003), they only used the bone signals for adapting the recognizer.…”

Section: Introductionmentioning

confidence: 99%

“…Further, their model does not produce an enhanced waveform but rather only enhances features for speech recognition. Strand et al (2003) designed an ear plug to capture the vibrations in the ear canal, and used the signals for speech recognition with MLLR adaptation. Heracleous et al (2003) used a stethoscope device to capture the bone vibrations of the head and used that for non-audible murmur recognition.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multisensory processing for speech enhancement and magnitude-normalized spectra for speech modeling

Subramanya¹,

Zhang

Liu

et al. 2008

Speech Communication

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multisensory processing for speech enhancement and magnitude-normalized spectra for speech modeling

Subramanya¹,

Zhang

Liu

et al. 2008

Speech Communication

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“…In military operations, the environment often involves high levels of noise from factors such as blasts, gunshots, and aircraft. It has been well-documented that the performance of contemporary ASR systems is degraded by heavy background noise, leading to more word errors in speech recognition output [24][25][26][27][28][29]. Moreover, the noise in ASR audio input may result in specific types of word errors in the output text interfering with the documentation when extracting relevant medical information.…”

Section: Introductionmentioning

confidence: 99%

Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study

Woo¹,

Mishra²,

Lin³

et al. 2021

JMIR Mhealth Uhealth

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Background Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. Objective The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation. Methods The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival. To this end, the state-of-the-art automatic speech recognition (ASR) solutions with the following modular improvements were thoroughly explored: noise-resilient ASR, multi-style training, customized lexicon, and speech enhancement. The development of the platform was strictly guided by qualitative research and simulation-based evaluation to address the relevant challenges through progressive improvements at every process step of the end-to-end solution. The primary performance metrics included medical word error rate (WER) in machine-transcribed text output and an F1 score calculated by comparing the autogenerated documentation to manual documentation by physicians. Results The total number of 15,139 individual words necessary for completing the documentation were identified from all conversations that occurred during the physician-supervised simulation drills. The baseline model presented a suboptimal performance with a WER of 69.85% and an F1 score of 0.611. The noise-resilient ASR, multi-style training, and customized lexicon improved the overall performance; the finalized platform achieved a medical WER of 33.3% and an F1 score of 0.81 when compared to manual documentation. The speech enhancement degraded performance with medical WER increased from 33.3% to 46.33% and the corresponding F1 score decreased from 0.81 to 0.78. All changes in performance were statistically significant (P<.001). Conclusions This study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation.

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“…The bone-conductive microphone used in [2,3], the throat microphone used in [4], and the ear-plug used in [5] are acoustic sensors similar to NAM microphones. Basically, in those studies a nonconventional acoustic sensor combined with a standard microphone was used to increase the robustness against noise.…”

Section: Introductionmentioning

confidence: 99%

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Heracleous

Kaino

Saruwatari

et al. 2006

EURASIP J. Adv. Signal Process.

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We present the use of stethoscope and silicon NAM (nonaudible murmur) microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible) speech, but also very quietly uttered speech (nonaudible murmur). As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc.) for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a 93.9% word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

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On the feasibility of ASR in extreme noise using the PARAT earplug communication terminal

Cited by 12 publications

References 11 publications

Multisensory processing for speech enhancement and magnitude-normalized spectra for speech modeling

Multisensory processing for speech enhancement and magnitude-normalized spectra for speech modeling

Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

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