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

Automatic speech recognition research at NASA-Ames Research Center

A trainable acoustic pattern recognizer manufactured by Scope Electronics is presented. The voice command system VCS encodes speech by sampling 16 bandpass filters with center frequencies in the range from 200 to 5000 Hz. Variations in speaking rate are compensated for by a compression algorithm that subdivides each utterance into eight subintervals in such a way that the amount of spectral change within each subinterval is the same. The recorded filter values within each subinterval are then reduced to a 15-bit representation, giving a 120-bit encoding for each utterance. The VCS incorporates a simple recognition algorithm that utilizes five training samples of each word in a vocabulary of up to 24 words. The recognition rate of approximately 85 percent correct for untrained speakers and 94 percent correct for trained speakers was not considered adequate for flight systems use. Therefore, the built-in recognition algorithm was disabled, and the VCS was modified to transmit 120-bit encodings to an external computer for recognition

How expertise and language familiarity influence perception of speech of people with Parkinson’s disease

Parkinson’s disease (PD) is a progressive neurological disorder characterized by several motor and non-motor manifestations. PD frequently leads to hypokinetic dysarthria, which affects speech production and often has a detrimental impact on everyday communication. Among the typical manifestations of hypokinetic dysarthria, speech and language therapists (SLTs) identify prosody as the most affected cluster of speech characteristics. However, less is known about how untrained listeners perceive PD speech and how affected prosody influences their assessments of speech. This study explores the perception of sentence type intonation and healthiness of PD speech by listeners with different levels of familiarity with speech disorders in Dutch. We investigated assessments and classification accuracy differences between Dutch-speaking SLTs (n = 18) and Dutch/non-Dutch speaking untrained listeners (n = 27 and n = 124, respectively). We collected speech data from 30 Dutch speakers diagnosed with PD and 30 Dutch healthy controls. The stimuli set consisted of short phrases from spontaneous and read speech and of phrases produced with different sentence type intonation. Listeners participated in an online experiment targeting classification of sentence type intonation and perceived healthiness of speech. Results indicate that both familiarity with speech disorders and with speakers’ language are significant and have different effects depending on the task type, as different listener groups demonstrate different classification accuracy. There is evidence that untrained Dutch listeners classify PD speech as unhealthy more accurately than both trained Dutch and untrained non-Dutch listeners, while trained Dutch listeners outperform the other two groups in sentence type classification

A Database of Anechoic Microphone Array Measurements of Musical Instruments

A collection of 3305 single notes of 41 musical instruments of different historical periods was recorded and analyzed. The database includes the instrument recordings, radiation patterns (directivities), and audio features such as the sound power or spectral centroid along with information about the identity and the making of the instrument and its player. The database can be used in virtual reality applications such as room acoustic simulation and auralization, or for the study of musical instruments acoustics themselves

Social media for research discourse, dissemination, and collaboration in rheumatology

Social media has become an important venue for rheumatologists, patients, organizations, and other stakeholders to discuss recent research advances in diagnosis and management of rheumatic disorders. In this article, we describe the current state of how social media may enhance dissemination, discourse, and collaboration in rheumatology research. Social media may refer to social platforms like Twitter and Instagram or digital media like podcasts and other websites that are operated for providing as free, open-access medical education (FOAM). Twitter has been one of the most active social media venues and continues to host a vibrant rheumatology community. Examples of research discussions on Twitter include organic user tweets, educational threads ( tweetorials ), live-tweeting academic conferences, and journals posting recently-accepted articles. Some research collaborations have been initiated through social media interactions. Social media may also directly contribute to research by facilitating the recruitment of study participants and the collection of survey-based data. Thus, social media is an evolving and important tool to enhance research discourse, dissemination, and collaboration in rheumatology

The Ames Virtual Environment Workstation: Implementation issues and requirements

This presentation describes recent developments in the implementation of a virtual environment workstation in the Aerospace Human Factors Research Division of NASA's Ames Research Center. Introductory discussions are presented on the primary research objectives and applications of the system and on the system's current hardware and software configuration. Principle attention is then focused on unique issues and problems encountered in the workstation's development with emphasis on its ability to meet original design specifications for computational graphics performance and for associated human factors requirements necessary to provide compelling sense of presence and efficient interaction in the virtual environment