A Deep Echo State Neural Network is used to predict total intensity at a detector, standard devia... more A Deep Echo State Neural Network is used to predict total intensity at a detector, standard deviation of intensity over the area of a detector, and center-of-intensity for a deep turbulence example. A short description of the reason for choosing a Deep Echo State Network, as well as a full description of the network optimization and an example using 30 seconds of data is given. Specifically, indications are that this type of network can handle the nonstationary and nonlinear aspects of laser propagation through long distance deep atmospheric turbulence. The network shows a remarkable ability to predict future signals. At this time, more work needs to be done on optimizing the network to achieve even better results.
Journal of the Acoustical Society of America, Nov 1, 2013
Dysarthria affects approximately 46 million people worldwide, with three million individuals resi... more Dysarthria affects approximately 46 million people worldwide, with three million individuals residing in the US. Clinical intervention by speech-language pathologists (SLPs) in the United States is supplemented by high quality research, clinical expertise, and state of the art technology, supporting the overarching goal of improved communication. Unfortunately, many individuals do not have access to such care, leaving them with a persisting inability to communicate. Telemedicine, along with the growing use of mobile devices to augment clinical practice, provides the impetus for the development of remote, mobile applications to augment the work of SLPs. The proposed application will record speech samples and provide a variety of derived calculations, novel and traditional, to assess the integrity of speech production, including: vowel space area, assessment of an individual’s pathology fingerprint, and identification of which parameters of the intelligibility disorder are most disrupted (e.g., prosody, vocal quality). The individualized selection of desired information for incorporation into a report template will be available. The reports will mimic those generated manually by SLPs today. The automation of this assessment will allow SLPs to treat patients remotely, allowing for the widespread, worldwide impact of high skilled assessment, something currently lacking in underdeveloped parts of the world.
Perspectives of the ASHA Special Interest Groups, Aug 15, 2019
Purpose The use and study of formant frequencies for the description of vowels is commonplace in ... more Purpose The use and study of formant frequencies for the description of vowels is commonplace in acoustic phonetics and in attempts to understand results of speech perception studies. Numerous studies have shown that listeners are better able to distinguish vowels when the acoustic parameters are based on spectral information extracted at multiple time points during the duration of the vowel, rather than at a single point in time. The purpose of this study was to validate an automated method for extracting formant trajectories, using information across the time course of production, and subsequently characterize the formant trajectories of vowels using a large, diverse corpus of speech samples. Method Using software tools, we automatically extract the 1st 2 formant frequencies (F1/F2) at 10 equally spaced points over a vowel's duration. Then, we compute the average trajectory for each vowel token. The 1,600 vowel observations in the Hillenbrand database and the more than 50,000 vowel observations in the TIMIT database are analyzed. Results First, we validate the automated method by comparing against the manually obtained values in the Hillenbrand database. Analyses reveal a strong correlation between the automated and manual formant estimates. Then, we use the automated method on the 630 speakers in the TIMIT database to compute average formant trajectories. We noted that phonemes that have close F1 and F2 values at the temporal midpoint often exhibit formant trajectories progressing in different directions, hence highlighting the importance of formant trajectory progression. Conclusions The results of this study support the importance of formant trajectories over single-point measurements for the successful discrimination of vowels. Furthermore, this study provides a baseline for the formant trajectories for men and women across a broad range of dialects of Standard American English.
Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), 2022
Image feature shifts due to atmospheric refraction are predicted with a finite difference machine... more Image feature shifts due to atmospheric refraction are predicted with a finite difference machine learning method based on physics-infused modeling and data-driven training using time-lapse imagery. The model’s performance is compared with a previous approach.
Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Image feature shifts due to atmospheric refraction are predicted with a finite difference machine... more Image feature shifts due to atmospheric refraction are predicted with a finite difference machine learning method based on physics-infused modeling and data-driven training using time-lapse imagery. The model’s performance is compared with a previous approach.
There's a problem with your browser or settings. Your browser or your browser's setting... more There's a problem with your browser or settings. Your browser or your browser's settings are not supported. To get the best experience possible, please download a compatible browser. If you know your browser is up to date ...
Laser Communication and Propagation through the Atmosphere and Oceans X, 2021
This work presents an extended analysis of atmospheric refraction effects captured by time-lapse ... more This work presents an extended analysis of atmospheric refraction effects captured by time-lapse imagery for near-ground and near-horizontal paths. Monthly trends and multipath analysis of image shift caused by refraction during daytime are studied. Nighttime shift measurements during moonlit nights are also presented. Advanced nonlinear machine learning approaches for image shift prediction are implemented and the performance of the models is evaluated.
A Deep Echo State Neural Network is used to predict total intensity at a detector, standard devia... more A Deep Echo State Neural Network is used to predict total intensity at a detector, standard deviation of intensity over the area of a detector, and center-of-intensity for a deep turbulence example. A short description of the reason for choosing a Deep Echo State Network, as well as a full description of the network optimization and an example using 30 seconds of data is given. Specifically, indications are that this type of network can handle the nonstationary and nonlinear aspects of laser propagation through long distance deep atmospheric turbulence. The network shows a remarkable ability to predict future signals. At this time, more work needs to be done on optimizing the network to achieve even better results.
Journal of the Acoustical Society of America, Nov 1, 2013
Dysarthria affects approximately 46 million people worldwide, with three million individuals resi... more Dysarthria affects approximately 46 million people worldwide, with three million individuals residing in the US. Clinical intervention by speech-language pathologists (SLPs) in the United States is supplemented by high quality research, clinical expertise, and state of the art technology, supporting the overarching goal of improved communication. Unfortunately, many individuals do not have access to such care, leaving them with a persisting inability to communicate. Telemedicine, along with the growing use of mobile devices to augment clinical practice, provides the impetus for the development of remote, mobile applications to augment the work of SLPs. The proposed application will record speech samples and provide a variety of derived calculations, novel and traditional, to assess the integrity of speech production, including: vowel space area, assessment of an individual’s pathology fingerprint, and identification of which parameters of the intelligibility disorder are most disrupted (e.g., prosody, vocal quality). The individualized selection of desired information for incorporation into a report template will be available. The reports will mimic those generated manually by SLPs today. The automation of this assessment will allow SLPs to treat patients remotely, allowing for the widespread, worldwide impact of high skilled assessment, something currently lacking in underdeveloped parts of the world.
Perspectives of the ASHA Special Interest Groups, Aug 15, 2019
Purpose The use and study of formant frequencies for the description of vowels is commonplace in ... more Purpose The use and study of formant frequencies for the description of vowels is commonplace in acoustic phonetics and in attempts to understand results of speech perception studies. Numerous studies have shown that listeners are better able to distinguish vowels when the acoustic parameters are based on spectral information extracted at multiple time points during the duration of the vowel, rather than at a single point in time. The purpose of this study was to validate an automated method for extracting formant trajectories, using information across the time course of production, and subsequently characterize the formant trajectories of vowels using a large, diverse corpus of speech samples. Method Using software tools, we automatically extract the 1st 2 formant frequencies (F1/F2) at 10 equally spaced points over a vowel's duration. Then, we compute the average trajectory for each vowel token. The 1,600 vowel observations in the Hillenbrand database and the more than 50,000 vowel observations in the TIMIT database are analyzed. Results First, we validate the automated method by comparing against the manually obtained values in the Hillenbrand database. Analyses reveal a strong correlation between the automated and manual formant estimates. Then, we use the automated method on the 630 speakers in the TIMIT database to compute average formant trajectories. We noted that phonemes that have close F1 and F2 values at the temporal midpoint often exhibit formant trajectories progressing in different directions, hence highlighting the importance of formant trajectory progression. Conclusions The results of this study support the importance of formant trajectories over single-point measurements for the successful discrimination of vowels. Furthermore, this study provides a baseline for the formant trajectories for men and women across a broad range of dialects of Standard American English.
Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), 2022
Image feature shifts due to atmospheric refraction are predicted with a finite difference machine... more Image feature shifts due to atmospheric refraction are predicted with a finite difference machine learning method based on physics-infused modeling and data-driven training using time-lapse imagery. The model’s performance is compared with a previous approach.
Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Image feature shifts due to atmospheric refraction are predicted with a finite difference machine... more Image feature shifts due to atmospheric refraction are predicted with a finite difference machine learning method based on physics-infused modeling and data-driven training using time-lapse imagery. The model’s performance is compared with a previous approach.
There's a problem with your browser or settings. Your browser or your browser's setting... more There's a problem with your browser or settings. Your browser or your browser's settings are not supported. To get the best experience possible, please download a compatible browser. If you know your browser is up to date ...
Laser Communication and Propagation through the Atmosphere and Oceans X, 2021
This work presents an extended analysis of atmospheric refraction effects captured by time-lapse ... more This work presents an extended analysis of atmospheric refraction effects captured by time-lapse imagery for near-ground and near-horizontal paths. Monthly trends and multipath analysis of image shift caused by refraction during daytime are studied. Nighttime shift measurements during moonlit nights are also presented. Advanced nonlinear machine learning approaches for image shift prediction are implemented and the performance of the models is evaluated.
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Papers by Steven Sandoval