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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

Huang, Yiteng↗
NASA NTRS2009

Method and apparatus for obtaining complete speech signals for speech recognition applications

The present invention relates to a method and apparatus for obtaining complete speech signals for speech recognition applications. In one embodiment, the method continuously records an audio stream comprising a sequence of frames to a circular buffer. When a user command to commence or terminate speech recognition is received, the method obtains a number of frames of the audio stream occurring before or after the user command in order to identify an augmented audio signal for speech recognition processing. In further embodiments, the method analyzes the augmented audio signal in order to locate starting and ending speech endpoints that bound at least a portion of speech to be processed for recognition. At least one of the speech endpoints is located using a Hidden Markov Model.

Abrash, Victor↗
NASA NTRS1991

Syntactic error modeling and scoring normalization in speech recognition: Error modeling and scoring normalization in the speech recognition task for adult literacy training

The purpose was to develop a speech recognition system to be able to detect speech which is pronounced incorrectly, given that the text of the spoken speech is known to the recognizer. Better mechanisms are provided for using speech recognition in a literacy tutor application. Using a combination of scoring normalization techniques and cheater-mode decoding, a reasonable acceptance/rejection threshold was provided. In continuous speech, the system was tested to be able to provide above 80 pct. correct acceptance of words, while correctly rejecting over 80 pct. of incorrectly pronounced words.

Olorenshaw, Lex↗
NASA NTRS1988

Speech recognition in advanced rotorcraft - Using speech controls to reduce manual control overload

An experiment has been conducted to ascertain the usefulness of helicopter pilot speech controls and their effect on time-sharing performance, under the impetus of multiple-resource theories of attention which predict that time-sharing should be more efficient with mixed manual and speech controls than with all-manual ones. The test simulation involved an advanced, single-pilot scout/attack helicopter. Performance and subjective workload levels obtained supported the claimed utility of speech recognition-based controls; specifically, time-sharing performance was improved while preparing a data-burst transmission of information during helicopter hover.

Vidulich, Michael A.↗
NASA NTRS2006

Open Microphone Speech Understanding: Correct Discrimination Of In Domain Speech

An ideal spoken dialogue system listens continually and determines which utterances were spoken to it, understands them and responds appropriately while ignoring the rest This paper outlines a simple method for achieving this goal which involves trading a slightly higher false rejection rate of in domain utterances for a higher correct rejection rate of Out of Domain (OOD) utterances. The system recognizes semantic entities specified by a unification grammar which is specialized by Explanation Based Learning (EBL). so that it only uses rules which are seen in the training data. The resulting grammar has probabilities assigned to each construct so that overgeneralizations are not a problem. The resulting system only recognizes utterances which reduce to a valid logical form which has meaning for the system and rejects the rest. A class N-gram grammar has been trained on the same training data. This system gives good recognition performance and offers good Out of Domain discrimination when combined with the semantic analysis. The resulting systems were tested on a Space Station Robot Dialogue Speech Database and a subset of the OGI conversational speech database. Both systems run in real time on a PC laptop and the present performance allows continuous listening with an acceptably low false acceptance rate. This type of open microphone system has been used in the Clarissa procedure reading and navigation spoken dialogue system which is being tested on the International Space Station.

Hieronymus, James↗
NASA NTRS2017

Incorporating Speech Recognition into a Natural User Interface

The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to investigate options for a Speech Recognition System. To that end I attempted to integrate Sphinx4 into a user interface. Sphinx4 had great accuracy and is the only free program able to perform offline speech dictation. However it had a limited dictionary of words that could be recognized, single syllable words were almost impossible for it to hear, and since it ran on Java it could not be integrated into the Unity based NUI. PocketSphinx ran much faster than Sphinx4 which would've made it ideal as a plugin to the Unity NUI, unfortunately creating a C# wrapper for the C code made the program unusable with Unity due to the wrapper slowing code execution and class files becoming unreachable. Unity Grammar Recognizer is the ideal speech recognition interface, it is flexible in recognizing multiple variations of the same command. It is also the most accurate program in recognizing speech due to using an XML grammar to specify speech structure instead of relying solely on a Dictionary and Language model. The Unity Grammar Recognizer will be used with the NUI for these reasons as well as being written in C# which further simplifies the incorporation.

Chapa, Nicholas↗
NASA NTRS1999

Loss tolerant speech decoder for telecommunications

A method and device for extrapolating past signal-history data for insertion into missing data segments in order to conceal digital speech frame errors. The extrapolation method uses past-signal history that is stored in a buffer. The method is implemented with a device that utilizes a finite-impulse response (FIR) multi-layer feed-forward artificial neural network that is trained by back-propagation for one-step extrapolation of speech compression algorithm (SCA) parameters. Once a speech connection has been established, the speech compression algorithm device begins sending encoded speech frames. As the speech frames are received, they are decoded and converted back into speech signal voltages. During the normal decoding process, pre-processing of the required SCA parameters will occur and the results stored in the past-history buffer. If a speech frame is detected to be lost or in error, then extrapolation modules are executed and replacement SCA parameters are generated and sent as the parameters required by the SCA. In this way, the information transfer to the SCA is transparent, and the SCA processing continues as usual. The listener will not normally notice that a speech frame has been lost because of the smooth transition between the last-received, lost, and next-received speech frames.

Prieto, Jr., Jaime L.↗
NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗
NASA NTRS2016

Speech Alarms Pilot Study

Currently on the International Space Station (ISS) and other space vehicles Caution & Warning (C&W) alerts are represented with various auditory tones that correspond to the type of event. This system relies on the crew's ability to remember what each tone represents in a high stress, high workload environment when responding to the alert. Furthermore, crew receive a year or more in advance of the mission that makes remembering the semantic meaning of the alerts more difficult. The current system works for missions conducted close to Earth where ground operators can assist as needed. On long duration missions, however, they will need to work off-nominal events autonomously. There is evidence that speech alarms may be easier and faster to recognize, especially during an off-nominal event. The Information Presentation Directed Research Project (FY07-FY09) funded by the Human Research Program included several studies investigating C&W alerts. The studies evaluated tone alerts currently in use with NASA flight deck displays along with candidate speech alerts. A follow-on study used four types of speech alerts to investigate how quickly various types of auditory alerts with and without a speech component - either at the beginning or at the end of the tone - can be identified. Even though crew were familiar with the tone alert from training or direct mission experience, alerts starting with a speech component were identified faster than alerts starting with a tone. The current study replicated the results from the previous study in a more rigorous experimental design to determine if the candidate speech alarms are ready for transition to operations or if more research is needed. Four types of alarms (caution, warning, fire, and depressurization) were presented to participants in both tone and speech formats in laboratory settings and later in the Human Exploration Research Analog (HERA). In the laboratory study, the alerts were presented by software and participants were asked to identify the alert as quickly and as accurately as possible. Reaction time and accuracy were measured. Participants identified speech alerts significantly faster than tone alerts. The HERA study investigated the performance of participants in a flight-like environment. Participants were instructed to complete items on a task list and respond to C&W alerts as they occurred. Reaction time and accuracy were measured to determine if the benefits of speech alarms are still present in an applied setting.

Sandor, A.↗
NASA NTRS2000

Improved Speech Coding Based on Open-Loop Parameter Estimation

A nonlinear optimization algorithm for linear predictive speech coding was developed early that not only optimizes the linear model coefficients for the open loop predictor, but does the optimization including the effects of quantization of the transmitted residual. It also simultaneously optimizes the quantization levels used for each speech segment. In this paper, we present an improved method for initialization of this nonlinear algorithm, and demonstrate substantial improvements in performance. In addition, the new procedure produces monotonically improving speech quality with increasing numbers of bits used in the transmitted error residual. Examples of speech encoding and decoding are given for 8 speech segments and signal to noise levels as high as 47 dB are produced. As in typical linear predictive coding, the optimization is done on the open loop speech analysis model. Here we demonstrate that minimizing the error of the closed loop speech reconstruction, instead of the simpler open loop optimization, is likely to produce negligible improvement in speech quality. The examples suggest that the algorithm here is close to giving the best performance obtainable from a linear model, for the chosen order with the chosen number of bits for the codebook.

Juang, Jer-Nan↗
NASA NTRSDate not supplied

Speech-Enhanced and Context Dependent Alerts: Future Implications for Spacecraft Design Abstract

In the future, NASA missions will involve many different space vehicles, habitats, and surface assets working together to provide safe and productive living and working environments for crew. Because these systems will be provided by multiple commercial companies working with NASA, it will be very different from missions of the past, bringing new challenges. One of the challenges is related to whether NASA should move beyond simple tone annunciation alerting systems, to more advanced systems that include speech. The other is related to determining the level of consistency required of safety-critical alert systems across spacecraft. Two studies were completed to address these important issues. The first study investigated the advantages and disadvantages of a tone+speech alert relative to the traditional tone-only alert. Results indicate that speech-enhanced alerts initially take longer to silence (the default action to which NASA personnel are trained), due to the need to listen to the entire message, but ultimately provided for faster understanding of the alert situation. Speech-enhanced alerts were also preferred by a large majority of crew-like study participants. An unexpected finding from this first study was that participants took longer to respond to tone-only alerts when they were mixed with speech- enhanced alerts. Participants waited to hear the speech message even for alerts they were trained to know did not contain speech components. This performance error is believed to be due to negative transfer of training. A second study focused on task and alert performance using a common set of tones across two contexts (“vehicles”) versus performance with a different set of tones for each context (“vehicle”). Participants were able to manage two different alert sets successfully; results indicate that discriminability of the two alert sets played a major role in their success. Implications for the design of spacecraft alerts are discussed and future areas of research are identified.

alerts↗
NASA NTRS2023

Speech-Enhanced and Context Dependent Alerts: Future Implications for Spacecraft Design

In the future, NASA missions will involve many different space vehicles, habitats, and surface assets working together to provide safe, productive environments for crew. Because these systems will be provided by multiple commercial companies working with NASA, it will be very different from missions of the past, bringing new challenges. One of the challenges is related to whether NASA should move beyond simple tone annunciation alerting systems, to more advanced systems that include speech. The other is related to determining the level of consistency required of safety-critical alert systems across spacecraft. Two studies were completed to address these important issues. The first study investigated the advantages and disadvantages of a tone+speech alert relative to the traditional tone-only alert. Results indicate that speech-enhanced alerts initially take longer to silence (the default action to which NASA personnel are trained), due to the need to listen to the entire message, but ultimately provided for faster understanding of the alert situation. Speech-enhanced alerts were also preferred by a large majority of crew-like study participants. An unexpected finding was that participants took longer to respond to tone-only alerts that were heard in the same session as speech-enhanced alerts. Participants waited to hear a speech message even for alerts they were trained to know did not contain speech components. This performance error is believed to be due to negative transfer of training. A second study focused on task and alert performance using a common set of tones across two contexts (e.g., vehicles, habitats, suits) versus performance with a different set of tones for each context. Participants were able to manage two different alert sets successfully; results indicate that discriminability of the two alert sets played a major role in their success. Implications for the design of spacecraft alerts are discussed, and future areas of research are identified.

alerts↗
NASA NTRS1988

A dissociation of objective and subjective workload measures in assessing the impact of speech controls in advanced helicopters

Among the new technologies that are expected to aid helicopter designers are speech controls. Proponents suggest that speech controls could reduce the potential for manual control overloads and improve time-sharing performance in environments that have heavy demands for manual control. This was tested in a simulation of an advanced single-pilot, scout/attack helicopter. Objective performance indicated that the speech controls were effective in decreasing the interference of discrete responses during moments of heavy flight control activity. However, subjective ratings indicated that the use of speech controls required extra effort to speak precisely and to attend to feedback. Although the operational reliability of speech controls must be improved, the present results indicate that reliable speech controls could enhance the time-sharing efficiency of helicopter pilots. Furthermore, the results demonstrated the importance of using multiple assessment techniques to completely assess a task. Neither the objective nor the subjective measures alone provided complete information. It was the contrast between the measures that was most informative.

Vidulich, Michael A.↗
NASA NTRS2014

Signal Processing Methods for Removing the Effects of Whole Body Vibration upon Speech

Humans may be exposed to whole-body vibration in environments where clear speech communications are crucial, particularly during the launch phases of space flight and in high-performance aircraft. Prior research has shown that high levels of vibration cause a decrease in speech intelligibility. However, the effects of whole-body vibration upon speech are not well understood, and no attempt has been made to restore speech distorted by whole-body vibration. In this paper, a model for speech under whole-body vibration is proposed and a method to remove its effect is described. The method described reduces the perceptual effects of vibration, yields higher ASR accuracy scores, and may significantly improve intelligibility. Possible applications include incorporation within communication systems to improve radio-communication systems in environments such a spaceflight, aviation, or off-road vehicle operations.

human response to vibration↗
NASA NTRS1972

Asynchronous sampling of speech with some vocoder experimental results

The method of asynchronously sampling speech is based upon the derivatives of the acoustical speech signal. The following results are apparent from experiments to date: (1) It is possible to represent speech by a string of pulses of uniform amplitude, where the only information contained in the string is the spacing of the pulses in time; (2) the string of pulses may be produced in a simple analog manner; (3) the first derivative of the original speech waveform is the most important for the encoding process; (4) the resulting pulse train can be utilized to control an acoustical signal production system to regenerate the intelligence of the original speech.

Babcock, M. L.↗
NASA NTRS1977

Effects of interior aircraft noise on speech intelligibility and annoyance

Recordings of the aircraft ambiance from ten different types of aircraft were used in conjunction with four distinct speech interference tests as stimuli to determine the effects of interior aircraft background levels and speech intelligibility on perceived annoyance in 36 subjects. Both speech intelligibility and background level significantly affected judged annoyance. However, the interaction between the two variables showed that above an 85 db background level the speech intelligibility results had a minimal effect on annoyance ratings. Below this level, people rated the background as less annoying if there was adequate speech intelligibility.

Pearsons, K. S.↗