DESIPHER Speech Degradation as an Indicator of Physiological Degeneration in ALS
Conditions: Amyotrophic Lateral Sclerosis
Sponsor: VA Office of Research and Development

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Investigator’s Protocol 
Title:  
DESIPHER Speech Degradation as an Indicator of Physiological Degeneration in ALS 
 
Principal Investigator:  
Samuel Phillips, PhD 
 
Co-Investigators:  
Elamin Elamin, MD 
 
Research Site: 
James A. Haley Veterans’ Hospital 
 
Sponsor:  
VA Rehabilitation Research and Development 
 
Project Number:  
1I21RX001902-01A1 
 
Rationale 
A disease called Amyotrophic lateral sclerosis (or ALS), which leads to difficulty 
swallowing, breathing, and movement, has been found to be higher for those serving 
in the military than in the general population. There are approximately 4,200 Veterans 
with ALS and roughly 1,000 new cases each year. When doctors attempt to determine 
the degree to which an ALS patient is suffering from the disease, they apply tests that 
are “graded” by experts. However, this approach to testing patients may not be very 
accurate. Researchers aim to use a system called DESIPHER to “listen” to ALS 
patients and find speech mistakes related to their condition. Researchers believe that, 
by detecting different types of errors, DESIPHER serves as a new kind of indicator of 
medical problems such as difficulty breathing or swallowing, without human “grading”. 
This may also lead to a better electronic system for understanding ALS patients’ 
speech, even with lots of errors. 
 
Hypotheses: Using a longitudinal study of Veterans with recently diagnosed with ALS, 
we propose to test the following two hypotheses: 
H1: Automatic speech recognition can be achieved for ALS patients, whose speech 
diverges from a baseline, as established from transcribed speech data, using speech-
model adaptation. 
H2: Divergence of speech from baseline will serve as a marker for disease 
progression. 
 
Specific Aims: Research questions to be addressed by this study are: 
Q1: Is it possible to train a speech recognition system to adapt to increasingly more 
frequent language/speech errors of particular types, to produce an accurate textual 
transcript that would be readable by a caregiver or physician? 
Q2: Are specific changes in physiological functioning (Forced Vital Capacity, tongue 

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strength, speech velocity, weight loss, aspiration risk, or psychological distress) 
associated with different types of language/speech errors? 
 
Significance: Results of the proposed study are expected to improve our ability to 
appropriately identify and intervene when Veterans with ALS are at risk of serious 
adverse medical issues such as respiratory failure and aspiration. Detection of speech 
divergence may serve as an additional assessment tool that requires much less effort 
from the patient than existing clinical measures. A future study may incorporate the 
divergence voice software into common technology to prolong the Veterans' ability to 
use their natural voice to control the environment around them. A further study may 
investigate if monitoring changes in language divergence, which indicate disease 
progression, could improve timely access when further appropriate interventions might 
be indicated. 
 
Relevance: In 2008, Amyotrophic lateral sclerosis (ALS) or Lou Gehrig’s Disease 
became a presumptively compensable (service connected) disease as the Institute of 
Medicine (IOM) Committee stated an association between the development of ALS and 
military service. According to the IOM report, military service increases life risk of ALS 
by 1.5 fold. There are approximately 4,200 Veterans with ALS and roughly 1,000 new 
cases each year. At the Tampa VA, since 2007, there has been a consistent rise in the 
number of Veterans diagnosed and treated with ALS. 
 
Background 
Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive, neurodegenerative 
disease that involves upper and lower motor neurons and is the most common form of 
motor neuron disease. ALS is characterized by muscle atrophy, muscle weakness, 
muscle spasticity, hyperreflexia, difficulty speaking (dysarthria), difficulty swallowing 
(dysphagia), and difficulty breathing (dyspnea). A serious healthcare crisis is emerging 
from the lack of capacity to support ALS sufferers, who typically must rely on a 
caregiver (often a family member) to manage their daily activities and to assist in 
relaying information between medical professionals and the patient. Mean survival 
time is three to five years; however, death may occur within months, or survival may 
last decades. 
 
There are a number of standardized objective assessments of bulbar function in 
patients with ALS (Green et al., 2013); for example, breathing patterns, articulatory 
patterns, and voice loudness. These are generally measured by technologies that 
record chest wall movements, oral pressures and flows, oral movement and strength, 
and speech acoustics. However, these measures are focused on the form of speech 
output (e.g., hypernasality) rather than the content of the speech output in terms of 
dialectal variations.  Identifying such variations is contextually similar to detecting 
different accents and dialects in spoken English. 
 
Our speech research focuses on the detection of dialectal variations by identifying 
spoken language divergences along a range of different dimensions. We borrow the 
notion of divergence from the study of cross-linguistic variations (Dorr, 1993, 1994; 

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Dorr et al., 2002; Habash and Dorr, 2002), where a language is considered "close" to 
another language if it shares certain properties (but possibly not others), and "far" (i.e., 
divergent) if very few properties are shared. To illustrate the concept of divergence 
across languages, consider three properties: vocabulary, pronunciation, and syntactic 
structure. Table 1 shows the properties that are shared between Spanish and four 
other languages. The most radically divergent pair in the table is Spanish and 
Chinese, which do not share vocabulary, syntactic structure, or pronunciation. We 
note that one could apply the same sort of analysis within a single language, such as 
English, by examining different dialectal variations. For example, a native U.S. 
English speaker from Massachusetts is likely to pronounce certain terms like "car" in a 
way that is more closely aligned to the British English pronunciation (where the "r" is 
dropped – non-rhotic) than to that of a native U.S. English speaker from Alabama 
(rhotic). 
 
Table 1: Linguistic divergence across languages 
Spanish 
Portuguese 
French 
English 
Chinese 
Vocabulary 
X 
X 
X 
Syntactic structure 
X 
X 
Pronunciation 
X 
 
We propose to apply this same notion of divergence to the problem of "speech 
functioning," constraining our language pair to asymptomatic English speech 
compared to impaired English speech. In this case, the properties to be studied are 
finer grained than the ones shown in Table 1, in that they are aligned with articulatory 
patterns in speech. Although we expect speech divergences to develop independently 
of language accent and dialect, we expect that prior work on dialect adaptation (Lehr 
et al., 2014) will be relevant for tracking speech divergences even within a single 
language. Research on modeling non-native or dialectal speech (Livescu and Glass, 
2000) is a close approximation to what we propose for recognition of speech changes 
over time, though the focus of dialectal modeling is on detecting divergent content, not 
on discerning characteristics of the speech signal. In order to test our hypothesis that 
divergence from a baseline serves as a marker for disease progression (and that, in 
fact, specific speech properties vary systematically in impaired speech), the current 
proposed pilot is for native speakers only; this allows for variability in dialect, but not 
variability regarding the level of knowledge of the language, thus retaining only one 
variable: the level of progression of the disease over time. However, an important 
follow-up study is that of speech recognition of accented (non-native) speech, once 
the properties that vary systematically with disease progression have been carefully 
identified in this current pilot study. 
 
A significant aspect of the proposed work is the identification of possible speech-
related biomarkers to be examined for correlations with other aspects of physiological 
function. Some that have been published in the literature (e.g., Duffy, 2012; Rudzicz 
at al., 2014) that are relevant to dialectal variations, but have not heretofore been 
measured objectively through dialectal analysis, are those listed below: 
 
 

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• Imprecise consonants 
• Distorted vowels 
• Slow rate 
• Short phrases 
• Hypernasality 
• Continuous voicing 
• Distorted substitutions or 
additions
Table 2 is a notional table illustrating a range of articulatory properties for "baseline" 
English (as determined for a native English speaker who has no speech impairment, 
i.e. pre-symptomatic) compared to those exhibited during different stages of ALS—
early, mid, late—thus providing a framework for capturing the degree of divergence. 
The final column has the highest level of divergence from the baseline: imprecise 
consonants, distorted vowels, and continuous bilabials. 
 
Table 2: Articulatory divergence across stages of ALS 
"Baseline" English 
ALS English (early) 
ALS English (mid) 
ALS English (late) 
consonants 
imprecise 
imprecise 
imprecise 
vowels 
~baseline 
distorted 
distorted 
bilabials (b,m) 
~baseline 
~baseline 
hypernasal 
 
Biadsy et al. (2011) conducted a speech processing study similar to our divergence 
approach, investigating the variation of speech properties under intoxicated and sober 
conditions. Rudzicz et al. (2014) employed another approach for recognizing impaired 
speech for detection of Alzheimer’s, and Little (2012) developed an analogous 
application for detection of Parkinson’s, as reported by Kepes (2014). These 
approaches measured the voice signal to answer a yes/no question (presence or 
absence of impairment), rather than analyzing the signal to determine the degree of 
divergence from a baseline. Nevertheless, the incidental but significant discovery from 
these studies, as well as related work of Beukelman et al. (2011), Green et al. (2013), 
and Orimaye et al. (2014)—that pronunciation varies systematically within categories of 
speech impairment—is a critical finding that can be leveraged for correlating the 
divergence from baseline English to different stages of ALS (i.e., the degree of 
biophysical degradation). 
 
A large part of our investigation will involve the characterization of divergence types, 
most notably: pronunciation divergences (articulatory and acoustic distinctions) and 
discourse divergences (word choice, duration of utterances, and disfluencies). Table 
3 illustrates these distinctions using examples of divergent speech characteristics that 
have been reported by Duffy (2012). In our proposed pilot study, we would expect to 
parameterize some—but perhaps not all— of these characteristics, as we have 
reports from clinician partners conducting a pilot study on ALS patients that 
phenomena such as these are common in the data they have collected so far (which 
will form our initial dataset, as described in Data Collection Protocol). 
 
Table 3: Examples of Divergent Speech Characteristics 
Level 
Speech 
Characteristics 
Example 
Comparable ALS 
Utterance 
Divergence Dimensions 
Pronunciation Vowel 
I wrote to the owner I wr[aw]t to the 
Substitution of back vowels 

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distortion 
[ooh]ner 
(oh/aw, oh/ooh) for front 
vowels 
Involuntary 
Repetition 
The best part of the 
movie was the 
surprise ending. 
[The bbbbest thing] * 
the best thing I 
wanted to tell you 
is… 
Prolonged or repeated sounds 
and syllables, possibly revised 
Imprecise 
consonants 
The blue shirt didn't 
feel comfortable. 
I wasn't comforful in 
that shirt. 
Deletion of soft alveolar stop 
([t]/[d]) 
Discourse 
Short phrases I don't feel like a very 
accomplished chef, 
although I've made 
eggplant parmesan. 
So how do you make 
this soup? Well I'm 
not so sure. See I 
don't know if I'd go 
that far. 
Short discourse words (so, 
well, see), small number of 
words per sentence, omission 
of large content words 
Slow rate 
I take pride in being 
able to cook myself. 
Um I do uh some uh 
cooking myself. 
Number of words per minute, 
frequency and pattern of 
unfilled or filled pauses (um, 
uh). 
Disfluencies 
I walked the dog to 
the store. 
I saw [the dog] * I 
mean the cat. 
Substitutions, possibly edited, 
e.g., I mean, sorry, excuse 
me, rather 
 
By understanding how speech deterioration correlates with other physiological 
degradation, it may be possible to apply a new, non-invasive measure for assessing 
the functionality of an ALS patient. In addition, the features associated with speech 
deterioration enable the adaptation of existing speech recognition software to a 
patient's speech as it evolves over time, so that the quality of life for patients may be 
improved through conversation with a computer. 
 
Work accomplished by PI 
Reducing Barriers to living at home for the rural Veteran with Amyotrophic Lateral 
Sclerosis (ORH N08-FY14Q4-S0-P01503, PI: Phillips). This project consists of 
interviews with caregivers and Veterans with ALS about the challenges of living and 
the barriers to maintaining living at home. This study is currently ongoing, but 
preliminary results point to timely and appropriate access to care as an important 
feature. Communication and transportation issues also present as consistent 
themes. 
 
Increasing Access to pulmonary function testing for rural veterans with ALS through at 
home testing (ORH FY 2015, PI: Phillips). Begun in Oct 2014, this program provides 
in-home testing for forced vital capacity (FVC). FVC is an important measure in this 
study. Additionally, telehealth appointments will be available while the respiratory 
therapist is in the home to consult with other members of the ALS team. This program 
may provide important access to the population in this study. 
 
"Machine Translation Divergences: A Formal Description and Proposed Solution," 
Computational Linguistics, 20:4, 1994. Dr. Bonnie J. Dorr was a NSF Presidential 
Faculty Fellow Awardee in 1997 for her seminal work on the problem of translating 
from one language into another in the face of language divergences (i.e., cross-
linguistic distinctions). Her work resulted in a systematic solution to the divergence 

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problem based on the formalization of linguistically-motivated classes that facilitate 
the design and implementation of a language processing system that is general 
enough to be applied to problems in text processing, speech processing, and language 
translation. 
 
Her subsequent research on cross-language divergence analysis for statistical word-
level alignment (Dorr et al., 2002) was successful for analysis of both minimally 
divergent language pairs (Spanish and English) and maximally divergent language 
pairs (Spanish and Chinese). Her experience in studying divergent speech patterns 
will be extremely valuable in leading data analysis efforts of ALS patient speech at 
Florida IHMC. 
 
Research Plan 
Population: Participants with ALS receiving medical services at James A. Haley 
Veterans’ Hospital.  According to the National VA ALS registry, one of the highest 
concentrations of Veterans with ALS is in Mid-Florida. In FY13, 136 Veterans with 
ALS were served by the Tampa VA. All veterans who meet inclusion criteria will be 
approached about the study.  
 
Inclusion criteria: Veterans have been diagnosed with ALS, are native monolingual 
speakers of American English, have bulbar involvement identified during initial ALS 
inpatient evaluation, a forced vital capacity (FVC) of greater than 50% of the expected 
value for age, and an ALSFRS-R score of 40 or greater.  
 
Exclusion criteria: a diagnosis of dementia, FVC less than 50%, inability to speak, or 
inability to follow directions. 
 
Number of subjects: We will recruit 20 Veterans with ALS who currently have bulbar 
involvement into the study. The process of recruitment begins when potential 
recruitees are initially assessed in the ALS clinic. The examining physician will make 
the potentially subject aware of the study.   If the potential subject is interested, they 
will be referred to the research assistant, who will inform the subject about the study, 
and begin the consent process if the potential subject is interested in participating. 20 
control healthy subjects without a diagnosed ALS will be recruited.  A ONETIME 
recording will be done of the TIMIT and Ba repetition. 
 
Timeline: Veterans will provide data quarterly (every 3 months) for two years for 8 
visits total. Each time the Veteran visits the Tampa VA, the research assistant will 
make a set of voice recordings. If the Veteran is not able to come to the VA within a 3 
month time period, the research assistant will make a home visit to make the recording. 
The control 20 healthy subjects without a diagnosed ALS will be a ONETIME 
recording of the TIMIT and Ba repetition. 
 
Data Collected: In order to minimize the burden to the subjects, research data collection 
and clinical data collection is being coordinated. The following data is collected per 
standard of care each time the Veteran is seen  

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Potential participants will be screened and evaluated for Amyotrophic lateral sclerosis (or 
ALS) within the Tampa VA systems of care. After screening a letters will be mailed about 
the study to these potential participants.  For those who respond to the letter showing 
interest in participating in the study and who are eligible, an appointment will be 
scheduled to meet for the consenting and enrollment process. There will be Waiver of 
document; for control 20 healthy subjects without a diagnosis of ALS. Script for waiver 
of Documentation will be used to recruit the 20 healthy subjects. 
 
This information will be extracted by the Research Assistant from participants’ medical 
records on a quarterly basis: 
1. Forced Vital Capacity (FVC),  
2. Penetration-Aspiration Scale (a paper-pencil screen; Rosenbek, 1996),  
3. ALSFRS-R (Cedarbaum, 1999), and  
4. Speech Intelligibility Test (SIT; Beukelman et al., 2007). 
5. Ba repetition test ( Rong et al., 2015).  
If any measurements are missing, the research assistant will confirm collection of 
clinical measurements and coordinate with the clinical team.  
 
The research assistant will measure tongue strength using the Iowa Oral Performance 
Instrument (IOPI Medical, Redmond, WA), and speech recordings made of four 
preselected passages. These passages, selected from the Texas-Instrument/MIT 
(TIMIT) corpus (Garofolo et al., 1993), combat what would generally lead to 
unintelligibility in speech recognition by providing a "ground truth"; with the knowledge of 
what the sentence is in advance, even if the patient is not otherwise able to speak 
intelligibly, the speech recognition system can be appropriately calibrated to expect 
certain levels of deviation from a baseline. A n d ,  the ‘Ba’ repetition test ( Rong et al., 
2015). The diadochokinetic (DDK) rate test in which the participants were asked to 
clearly and rapidly repeat /ba/ was used in (Rong et al. 2015), to help with speech 
intelligibility deterioration as a result neurologic atrophy. While this pilot study focuses 
on speech recognition that accommodates varying levels of impairment, in later stages 
of the disease, adaptive speech recognition technology would be most useful in 
combination with alternative and augmentative communication (AAC) technologies and 
brain computer interfaces (BCI); this powerful combination would accommodate typing, 
gaze, and head-mouse input, in addition to speech input (see related discussion in Dorr 
et al., 2015). 
 
We would expect to follow a patient from enrollment to the end of the study, or end of 
life. When possible, speech recordings and assessments will be coordinated during the 
quarterly visits to Tampa VA. If not possible, separate visits will be scheduled. If the 
patient were unable to come to Tampa VA, home visits would be scheduled. If results 
are promising, we would seek additional funds to follow this cohort beyond the end of 
study. 
 
The 20 control speakers will participate in ONETIME only, recording during the rest of 
the study time of the TIMIT Sentences and Ba repetition.  

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Participants diagnosed with ALS CPRS notes will be reviewed for the following co-
existing conditions to be found on the CPRS Notes: 
 
I. 
Parkinson's disease  
II. 
Multiple Systems Atrophy 
III. 
Olivopontinocerebellar degeneration 
IV. 
Shy Drager syndrome 
V. 
Progressive Supra nuclear palsy 
VI. 
Any kind of laryngeal dystonia 
Data Analysis: Data analysis will be conducted at the Florida Institute of Human and 
Machine Cognition (IHMC) in Ocala, Florida. Data sharing and security will be governed 
under the IPA agreement between James A. Haley Veterans’ Hospital and IHMC. 
Phonetic annotation of the training data is required in instances where we want to 
identify speech characteristics in the transcribed text (e.g., hypernasality). The Tampa 
VA will provide transcribed, annotated speech data (in addition to the audio recordings) 
to IHMC for setting up the baseline speech recognition system. To perform quality 
annotation, we will utilize a consistent set of guidelines to ensure that the system can 
learn patterns to aid in understanding speech with errors. Speech recordings will be 
separately annotated for speech irregularities by two research assistants using the 
annotation guidelines, and differences in annotations will be arbitrated by the PI or his 
designee. The annotated script, along with the original recording, will be de-identified 
and sent to IHMC using Cradeon, a FIPS 140-2 compliant encryption program. Sample 
annotations are provided below. It is critical that the degree to which the ALS has 
progressed be associated with each transcript, so that a different set of speech models 
can be produced for different degrees of ALS progression. 
 
From the audio recordings collected, 90% will be used for training the system. A 
randomly selected 10% will be used to test the divergence construct. 
 
Risk/Benefit Assessment 
There are minimal risks to the subject. The subject could fatigue, in which case they 
would be allowed to rest until they were ready to continue. Subjects could become 
frustrated while reading the passage. In that case, subjects would be given a break, 
and then continue with the protocol. There are no known benefits to participating in this 
research study. 
 
The etiology of ALS frequently creates other health issues. These anticipated issues 
that may require medical intervention and hospitalization include, but are not limited to: 
• Respiratory Insufficiency 
• sleep apnea 
• malnutrition 
• dehydration 
• tracheotomy 
• frontotemporal dementia 
• falls 
 

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Data Safety Monitoring Plan 
A data and safety monitoring plan will be implemented to insure that there are no 
changes in the benefit/risk ratio during the study and that confidentiality of research 
data is maintained. The PI, Co-PI, and research staff will be responsible for data 
safety and monitoring. All data obtained in the course of the study will be confidential. 
All participants will be informed of their confidentiality rights at the time informed 
consent is administered. Data collection, management and destruction will be 
conducted in strict accordance with the policies and procedures set forth by the 
Tampa VA Research & Development Office Research Data Security and Privacy 
Policy. Data will be stored on the password-protected research server which sits 
behind the VA firewall. All data will be stored and maintained in a Microsoft SQL 
Server or Microsoft Access back-end database. The server is managed by VATAM 
OI&T and data are backed up on a nightly basis. The following staff members will have 
access to the data once it is saved on the research server: the PI and the project staff. 
If a staff member leaves the project, an amendment to the IRB will remove their 
access to identifiable patient information for the study. 
 
Patients and providers will be assigned a number at the time of enrollment in the study 
and will be identified by that number during the course of the study and data analysis. 
Identifiable personal information will be stripped from the database so as to protect 
confidentiality. Monthly meetings of the investigators will include a discussion of issues 
pertaining to the assurance of patient confidentiality, and any needed changes will be 
instituted immediately to assure optimal patient protection. Breaches of confidentiality 
will be brought to the attention of the appropriate persons immediately as will any 
proposed changes to the data safety and monitoring plan 
 
Process for Obtaining Informed Consent and Protecting Patient Privacy 
Eligible participants will be referred to the Research Assistant who will review in detail 
the consent form and study procedures.  After participants have acknowledged 
complete understanding of the study, they will be asked to sign a consent form.  
Subjects will also be given a copy of the signed consent and will be advised of their 
rights to withdraw from the study at any time without penalty. 
 
Process for assessing ALS-related dementia:  
The cognitive ability of this population is closely followed by the clinical team.  The 
chart will be reviewed for competence to consent.  If no assessment score is available, 
the RA will administer the exam.  The Mini-Mental Exam will be used for assessing 
dementia.  The cut score for competence to consent is established in the literature.   
 
Documentation of Informed Consent 
The Research Assistant will enter progress notes for study enrollment into the CPRS 
medical records for each subject after consent forms have been signed.  A copy of the 
consent form will be placed in the participant’s CPRS medical record and a copy will be 
maintained with the study records which will remained locked in a file cabinet in the 
Research Assistant’s office. 
 
Payment to Subjects for Their Participation 

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Participants will be compensated $25 in check form or direct deposit per session visit for 
up to eight study visits.  
 
Provisions for Data Storage and Confidentiality 
All electronic data will be stored in 
R:\PROJECTS_CURRENT\Phillips_DESIPHER_RR&D_SPIRE\Secure.  All original 
documents (i.e. consent forms) will be stored in a locked file cabinet in PI’s office 147A, Grand 
Oaks Building.  
 
Each subject will be given a unique subject number. All data will be stored under that subject 
number. The PI will hold an electronic list with the map between subject number and patient 
information. All information sent to colleagues at IHMC will be de-identified. There is minimal 
risk of loss of identifiable information. 
 
Dissemination of Research Results 
The PI will be the take the lead in writing up the results of this work.   
 
List of References 
Beukelman, D.R., K. Yorkston, M. Hakel, and M. Dorsey. Speech Intelligibility Test 
(SIT): Computer Software. Lincoln, NE: Madonna Rehabilitation Hospital, 2007. 
Beukelman, David, Susan Fager, and Amy Nordness, "Communication Support for 
People with ALS," Neurology Research International, 2011. 
Biadsy, Fadi, William Yang Wang, Andrew Rosenberg, "Intoxication Detection using 
Phonetic, Phonotactic and Prosodic Cues," Proceedings of Interspeech, 2011. 
Cedarbaum, J.M., N. Stambler, E. Malta, C. Fuller, D. Hilt, B. Thurmond, A. 
Nakanishi, "The ALSFRS-R: a revised ALS functional rating scale that 
incorporates assessments of respiratory function," Journal of the 
Neurological Sciences, 169(1-2):13-21, 1999. 
Dorr, Bonnie J., Machine Translation: A View from the Lexicon, MIT Press, Cambridge, 
MA, 1993. Dorr, Bonnie J., "Machine Translation Divergences: A Formal 
Description and Proposed Solution,"Computational Linguistics, 20:4, pp. 597--
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in Proceedings of the Fifth Conference of the Association for Machine 
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Dorr, Bonnie J., Lucian Galescu, Edward J. Golob, K. Brent Venable, and Yorick 
Wilks, "Companion-based Ambient Robust Intelligence (CARING)," 
Proceedings of the AAAI Workshop on Artificial Intelligence Applied to 
Assistive Technologies and Smart Environments, 2015. 
Duffy, Joseph, Motor Speech Disorders: Substrates, Differential Diagnosis, and 
Management, 3
rd edition, 2012. 
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Dahlgren, Victor Zue. "TIMIT Acoustic-Phonetic Continuous Speech Corpus," 
LDC93S1. Web Download. 
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http://web.mit.edu/course/6/6.863/share/nltk_lite/timit/allsenlist.txt 
Habash, Nizar and Bonnie J. Dorr, "Handling Translation Divergences: Combining 
Statistical and Symbolic Techniques in Generation-Heavy Machine 
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Synhorst, Lorne Zinman, James D. Berry, "Bulbar and speech motor 
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