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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Speech Alarms Pilot Study

Speech alarms have been used extensively in aviation and included in International Building Codes (IBC) and National Fire Protection Association's (NFPA) Life Safety Code. However, they have not been implemented on space vehicles. Previous studies conducted at NASA JSC showed that speech alarms lead to faster identification and higher accuracy. This research evaluated updated speech and tone alerts in a laboratory environment and in the Human Exploration Research Analog (HERA) in a realistic setup.

Sandor, Aniko↗
NASA NTRS2018

Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.

E L Meszaros↗
NASA NTRS2018

Automatic Speech Recognition for Launch Control Center Communication Using Recurrent Neural Networks with Data Augmentation and Custom Language Model

Transcribing voice communications in NASA’s launch control center is important for information utilization. However, automatic speech recognition in this environment is particularly challenging due to the lack of training data, unfamiliar words in acronyms, multiple different speakers and accents, and conversational characteristics of speaking. We used bidirectional deep recurrent neural networks to train and test speech recognition performance. We showed that data augmentation and custom language models can improve speech recognition accuracy. Transcribing communications from the launch control center will help the machine analyze information and accelerate knowledge generation.

Chow, Edward↗
NASA NTRS1964

Speech communications

Analysis and synthesis of speech, and use of procedures for studying human speech processes

SPEECH↗
NASA NTRS1967

Speech communication

Linguistic and speech communication concepts on mechanism of glottal vibration for vowels and consonants, and homomorphic speech processing

COMMUNICATION SYSTEM↗
NASA NTRS1994

Multichannel spatial auditory display for speech communications

A spatial auditory display for multiple speech communications was developed at NASA/Ames Research Center. Input is spatialized by the use of simplified head-related transfer functions, adapted for FIR filtering on Motorola 56001 digital signal processors. Hardware and firmware design implementations are overviewed for the initial prototype developed for NASA-Kennedy Space Center. An adaptive staircase method was used to determine intelligibility levels of four-letter call signs used by launch personnel at NASA against diotic speech babble. Spatial positions at 30 degrees azimuth increments were evaluated. The results from eight subjects showed a maximum intelligibility improvement of about 6-7 dB when the signal was spatialized to 60 or 90 degrees azimuth positions.

NASA Discipline Space Human Factors↗
DOE OSTI2025

Automatic speech recognition predicts contemporaneous earthquake fault displacement

Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.

58 GEOSCIENCES↗
DOE OSTI2023

Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition

Over the past decade, the machine learning security community has developed a myriad of defenses for evasion attacks. An understudied question in that community is: for whom do these defenses defend? This work considers common approaches to defending learned systems and how security defenses result in performance inequities across different sub-populations. We outline appropriate parity metrics for analysis and begin to answer this question through empirical results of the fairness implications of machine learning security methods. We find that many methods that have been proposed can cause direct harm, like false rejection and unequal benefits from robustness training. The framework we propose for measuring defense equality can be applied to robustly trained models, preprocessing-based defenses, and rejection methods. We identify a set of datasets with a user-centered application and a reasonable computational cost suitable for case studies in measuring the equality of defenses. In our case study of speech command recognition, we show how such adversarial training and augmentation have non-equal but complex protections for social subgroups across gender, accent, and age in relation to user coverage. We present a comparison of equality between two rejection-based defenses: randomized smoothing and neural rejection, finding randomized smoothing more equitable due to the sampling mechanism for minority groups. This represents the first work examining the disparity in the adversarial robustness in the speech domain and the fairness evaluation of rejection-based defenses.

• Artificial intelligence (AI) / machine learning ↗
NASA NTRS1975

Techniques for decoding speech phonemes and sounds: A concept

Techniques studied involve conversion of speech sounds into machine-compatible pulse trains. (1) Voltage-level quantizer produces number of output pulses proportional to amplitude characteristics of vowel-type phoneme waveforms. (2) Pulses produced by quantizer of first speech formants are compared with pulses produced by second formants.

Lokerson, D. C.↗
NASA NTRS1975

On hemispheric differences in evoked potentials to speech stimuli

Confirmation is provided for the belief that evoked potentials may reflect differences in hemispheric functioning that are marginal at best. Subjects were right-handed and audiologically normal men and women, and responses were recorded using standard EEG techniques. Subjects were instructed to listen for the targets while laying in a darkened sound booth. Different stimuli, speech and tone signals, were used. Speech sounds were shown to evoke a response pattern that resembles that to tone or clicks. Analysis of variances on peak amplitude and latency measures showed no significant differences between hemispheres, however, a Wilcoxon test showed significant differences in hemispheres for certain target tasks.

Galambos, R.↗
NASA NTRS1975

On hemispheric differences in evoked potentials to speech stimuli

Subjects were asked to count the number of times a 'target' sound occurred in lists of speech sounds (pa or ba) or pure tones (250 or 600 c/sec) in which one of the sounds (the 'frequent') appeared about four times as often as the target. The response to both targets and frequents were separately averaged from electrodes at vertex at symmetrical left and right parietal locations. The expected sequence of deflections, including P3 waves with about 350 msec latency, was found in the responses to target stimuli. Very little difference was found between the right and left hemispheric responses to speech or pure tones, either frequent or target.

Galambos, R.↗
NASA NTRS1977

Speech as a pilot input medium

The speech recognition system under development is a trainable pattern classifier based on a maximum-likelihood technique. An adjustable uncertainty threshold allows the rejection of borderline cases for which the probability of misclassification is high. The syntax of the command language spoken may be used as an aid to recognition, and the system adapts to changes in pronunciation if feedback from the user is available. Words must be separated by .25 second gaps. The system runs in real time on a mini-computer (PDP 11/10) and was tested on 120,000 speech samples from 10- and 100-word vocabularies. The results of these tests were 99.9% correct recognition for a vocabulary consisting of the ten digits, and 99.6% recognition for a 100-word vocabulary of flight commands, with a 5% rejection rate in each case. With no rejection, the recognition accuracies for the same vocabularies were 99.5% and 98.6% respectively.

Plummer, R. P.↗
NASA NTRS1980

Development of a speech autocuer

A wearable, visually based prosthesis for the deaf based upon the proven method for removing lipreading ambiguity known as cued speech was fabricated and tested. Both software and hardware developments are described, including a microcomputer, display, and speech preprocessor.

Bedles, R. L.↗
NASA NTRS1984

Application of speech recognition and synthesis in the general aviation cockpit

Interactive speech recognition/synthesis technology is assessed as a method for the aleviation of single-pilot IFR flight workloads. Attention was given during this series of evaluations to the conditions typical of general aviation twin-engine aircrft cockpits, covering several commonly encountered IFR flight condition scenarios. The most beneficial speech command tasks are noted to be in the data retrieval domain, which would allow the pilot access to uplinked data, checklists, and performance charts. Data entry tasks also appear to benefit from this technology.

North, R. A.↗
NASA NTRS1984

Alerting prefixes for speech warning messages

A major question posed by the design of an integrated voice information display/warning system for next-generation helicopter cockpits is whether an alerting prefix should precede voice warning messages; if so, the characteristics desirable in such a cue must also be addressed. Attention is presently given to the results of a study which ascertained pilot response time and response accuracy to messages preceded by either neutral cues or the cognitively appropriate semantic cues. Both verbal cues and messages were spoken in direct, phoneme-synthesized speech, and a training manipulation was included to determine the extent to which previous exposure to speech thus produced facilitates these messages' comprehension. Results are discussed in terms of the importance of human factors research in cockpit display design.

Bucher, N. M.↗
NASA NTRS1985

Index to NASA news releases and speeches, 1984

The Index to NASA News Releases and Speeches (1984) contains selected speeches and news releases issued by NASA Headquarters during the year 1984. The index was prepared by the NASA Scientific and Technical Information Facility operated for the National Aeronautical and Space Administration by PRC Government Information Systems.

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