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The Vocal Joystick

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The Vocal Joystick is a novel human-computer interface mechanism designed to enable individuals with motor impairments to make use of vocal parameters to control objects on a computer screen (buttons, sliders, etc.) and ultimately electro-mechanical instruments (e.g., robotic arms, wireless home automation devices). We have developed a working prototype of our "VJ-engine" with which individuals can now control computer mouse movement with their voice. The core engine is currently optimized according to a number of criterion. In this paper, we describe the engine system design, engine optimization, and user-interface improvements, and outline some of the signal processing and pattern recognition modules that were successful. Lastly, we present new results comparing the vocal joystick with a state-of-the-art eye tracking pointing device, and show that not only is the Vocal Joystick already competitive, for some tasks it appears to be an improvement
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THE VOCAL JOYSTICK
Jeff A. Bilmes, Jonathan Malkin, Xiao Li, Susumu Harada§, Kelley Kilanski,
Katrin Kirchhoff, Richard Wright, Amarnag Subramanya, James A. Landay§,
Patricia Dowden, Howard Chizeck
Dept. of Electrical Engineering
§Dept. of Computer Science & Eng.
Dept. of Linguistics
Dept. of Speech & Hearing Science
University of Washington
Seattle, WA
ABSTRACT
The Vocal Joystick is a novel human-computer interface mecha-
nism designed to enable individuals with motor impairments to make
use of vocal parameters to control objects on a computer screen (but-
tons, sliders, etc.) and ultimately electro-mechanical instruments
(e.g., robotic arms, wireless home automation devices). We have
developed a working prototype of our “VJ-engine” with which indi-
viduals can now control computer mouse movement with their voice.
The core engine is currently optimized according to anumber of cri-
terion. In this paper, we describe the engine system design, engine
optimization, and user-interface improvements, and outline some of
the signal processing and pattern recognition modules that were suc-
cessful. Lastly, we present new results comparing the vocal joystick
with a state-of-the-art eye tracking pointing device, and show that
not only is the Vocal Joystick already competitive, for some tasks it
appears to be an improvement.
1. INTRODUCTION
Many existing human-computer interfaces (e.g., the mouse and key-
board, touch screens, pen tablets, etc.) are ill-suited to individuals
with motor impairments. Specialized (and often expensive) human-
computer interfaces have been developed specifically for this group,
including sip-and-puff switches [1], head mice [2, 1, 3], eye-gaze
and eye tracking devices [4], chin joysticks [5], and tongue switches
[6]. While many individuals with motor impairments have complete
use of their vocal system, these assistive devices do not make full
use of it. Sip and puff switches, for example, control a device by
sending binary signals and thus have relatively low communication
bandwidth, making it difficult to perform complex control tasks.
Natural spoken language is regarded as an obvious choice for
a human-computer interface. Despite significant research efforts in
automatic speech recognition (ASR), however, existing ASR sys-
tems are still not perfectly robust to a wide variety of speaking
conditions, noise, and accented speakers, and they have not yet
been universally adopted as a dominant human-computer interface.
In addition, while natural speech is optimal for human-to-human
communication, it may be sub-optimal for manipulating computers,
windows-icons-mouse-pointer (WIMP) interfaces, and other electro-
mechanical devices (such as a prosthetic robotic arm). Standard spo-
ken language commands, moreover, are ideal for discrete but not
for continuous operations. For example, to move a cursor from the
bottom-left to the upper-right of a screen, a user could repeatedly
This material is based on work supported by the National Science Foun-
dation under grant IIS-0326382.
utter “up” and “right”, or alternatively “stop” and “go” after setting
an initial trajectory and rate, but this can be inefficient. Other meth-
ods for using controlling mouse movement with speech have also
been developed [7, 8, 9] but none of these take advantage of the full
continuous nature of the human vocal system.
For the above reasons, we have developed an alternative voice-
based assistive technology termed the Vocal Joystick (VJ) [10]. Un-
like standard ASR, our system goes beyond the capabilities of se-
quences of discrete speech sounds, and exploits continuous vocal
characteristics such as pitch, vowel quality, and loudness which
are then mapped to continuous control parameters. Several video
demonstrations of the Vocal Joystick system are available online
http://ssli.ee.washington.edu/vj. In previous work,
we gave a high-level overview of the vocal joystick [10] and details
regarding motion acceleration [11] and adaptation [12, 13]. In this
work, we provide details about our design goals, the signal process-
ing and pattern recognition modules, and we report on a new user-
study that shows that the VJ compares favorably to a standard mod-
ern eye tracking device.
2. VOCAL JOYSTICK
The Vocal Joystick system maps from human vocalic effort to a set of
control signals used to drive a mouse pointer or robotic arm. It also
allows a small set of discrete spoken commands usable as mouse
clicks, button presses, and modality shifts. We use a “joystick” as
an analogy since it has the ability to simultaneously specify several
continuous degrees of freedom along witha small number of button
presses, and we consider this to be a generalization of a mouse.
In developing the VJ system, we have drawn on our ASR back-
ground to produce a system that, as best as possible, meets the fol-
lowing goals: 1) easy to learn: the VJ system should be easy to learn
and remember in order to keep cognitive load at a minimum. 2) easy
to speak: using a VJ-controlled device should not produce undue
strain on the human vocal system. It should be possible to use the
system for many hours at a time. 3) easy to recognize: the VJ sys-
tem should be as noise robust as possible, and should try to include
vocal sounds that are as acoustically distinct as possible. 4) percep-
tual: the VJ system should respect any perceptual expectations that
a user might have and also should be perceptually consistent (e.g.,
given knowledge of some aspects of a VJ system, a new vocal effort
should, say, move the mouse in an expected way). 5) exhaustive:
to improve communications bandwidth, the system should utilize as
many capabilities of the human vocal apparatus as possible, without
conflicting with goal 1. 6) universal: our design should use vocal
characteristics that minimize the chance that regional dialects, or ac-
cents will preclude its use. 7) complementary: the system should
be complementary with existing ASR systems. We do not mean to
replace ASR, but rather augment it. 8) resource-light: a VJ system
should run using few computational resources (CPU and memory)
and leave sufficient computational headroom for a base application
(e.g., a web browser, spreadsheet). 9) infrastructure: the VJ system
should be like a library, that any application can link to and use.
Unlike standard speech recognition, the VJ engine exploits the
ability of the human voice to produce continuous signals, thus go-
ing beyond the capabilities of sequences of discrete speech sounds
(such as syllables or words). Examples of these vocal parameters
include pitch variation, type and degree of vowel quality, and loud-
ness. Other possible (but not yet employed) qualities are degree of
vibrato, low-frequency articulator modulation, nasality, and velocity
and acceleration of the above.
2.1. Primary Vocal Characteristics
Three continuous vocal characteristics arecurrently extracted by the
VJ engine: energy,pitch, and vowel quality, yielding four simultane-
ous degrees of freedom. The first of these, localized acoustic energy,
is used for voice activity detection. In addition, it is normalized rela-
tive to the current detected vowel, and is used by our mouse applica-
tion to control the velocity of cursor movement. For example, a loud
voice causes a large movement while a quiet voicecauses a “nudge.
The second parameter, pitch, is also extracted but is currently unused
in existing applications (it thus constitutes a free parameter available
for future use). The third parameter is vowel quality. Unlike con-
sonants, which are characterized by a greater degree of constriction
in the vocal tract and which are inherently discrete in nature, vowels
are highly energetic and thus are well suited for environments where
both high accuracy and noise-robustness are crucial. Vowels can be
characterized using a 2-D space parameterized by F1 and F2, the first
and second vocal tract formants (resonant frequencies). We classify
vowels, however, directly and map them onto the 2-D vowel space
characterized by tongue height and tongue advancement (Figure 1)
(we found F1/F2 estimation to be too unreliable for this application).
In our initial VJ system, and in our VJ mouse control, we use the four
corners of this chart to map to the 4 principle directions of up, down,
left, and right as shown in Figure 1. We have also produced an 8-
and 9-class vowel system to enable more non-simultaneous degrees
of freedom and more precise specification of diagonal directions.
We also utilize a “neutral” schwa [ax] as a carrier vowel for when
other parameters (pitch and/or amplitude) are to be controlled with-
out any positional change. These other vowels (and their directional
correlates) are also shown in Figure 1.
In addition to the three continuous vocal parameters, “discrete
sounds” are also employed. We select (or reject) a candidate dis-
crete sound according to both linguistic criteria and the system cri-
teria mentioned in Section 2. So far, however, we have not utilized
more than two or three discrete sounds since our primary application
has been mouse control. Our research has thus focused on real-time
extraction of continuous parameters since that is less like standard
ASR technology.
3. THE VJ ENGINE
We have developed a portable modular library (the VJ engine) that
can be incorporated into a variety of applications. Following the
goals from Section 2, the engine shares common signal processing
operations in multiple modules, to produce real-time performance
while leaving considerable computational headroom for the applica-
tions being driven by the VJ engine.
Tongue Height
Front Central Back
High
Mid
Low
Tongue Advancement
[iy] [ix ] [uw ]
[ey] [ax ] [ow ]
[ae ] [a] [aa ]
[iy]
[ix ]
[uw ]
[ey ]
[ow ]
[ae ]
[a]
[aa ]
[ax ]
Fig. 1. Left:Vowel configurations as a function of their dominant ar-
ticulatory configurations. Right: Vowel-direction mapping: vowels
corresponding to directions for mouse movement in the WIMP VJ
cursor control.
Acoustic
Waveform Feature
Extraction
Features:
Energy
NCCF
F1/F2
MFCC
Signal
Processing
Energy
Vowel
Classification
Pattern
Recognition
Pitch
Tracking
Discrete Sound
Recognition
Motion
Parameters:
xy-directions,
Speed,
Acceleration,
Motion Control
Space
Transformation
Motion Computer
Interface
Driver Adaptation
Fig. 2. The vocal joystick engine system structure.
The VJ engine consists of three main components: acoustic sig-
nal processing,pattern recognition, and motion control (see Fig-
ure 2). First, the signal processing module extracts short-term
acoustic features, such as energy, autocorrelation coefficients, lin-
ear prediction coefficients and mel frequency cepstral coefficients
(MFCCs). These features are piped into the pattern recognition
module, where energy smoothing, pitch and formant tracking, vowel
classification and discrete sound recognition take place. This stage
also involves pattern recognition methods such as neural networks,
support vector machines (SVMs), and dynamic Bayesian networks
(see [12, 14, 15]). Finally, energy, pitch, vowel quality, and discrete
sounds become acoustic parameters that are transformed into direc-
tion, speed, and other motion-related parameters for the back-end
application.
An important first stage in the signal processing module is voice
activity detection (VAD). We categorize each frame into the three
separate categories silence,pre-active, or active, based on energy
and zero-crossing information. Pre-active frames may (or may not)
indicate the beginning of voice activity, for which only frontend fea-
ture extraction is executed. Active frames are those identified as
truly containing voice activity. Pattern recognition tasks, includ-
ing pitch tracking and vowel classification, are performed for these
frames. No additional computation is used for silence frames. If si-
lence frames occur after an unvoiced segment within a length range,
however, discrete sound recognition will be triggered.
The goal of the signal processing module is to extract low-level
acoustic features that can be used in estimating the four high-level
acoustic parameters. The acoustic waveforms are sampled at a rate
of 16,000 Hz, and a frame is generated every 10 ms. The extracted
frame-level features are energy, normalized cross-correlation coef-
ficients (NCCC), formants, and MFCCs [16, 17]. In addition, we
employ delta features and online (causal) mean subtraction and vari-
ance normalization.
Our pitch tracker module is based on several novel ideas. Many
pitch trackers require meticulous design of local and transition costs.
The forms of these functions are often empirically determined and
their parameters are tuned accordingly. In the VJ project, we use
a graphical modeling framework to automatically optimize pitch
tracking parameters in the maximum likelihood sense. Specifically,
we use a dynamic Bayesian network to represent the pitch- and
formant-tracking process and learn the costs using an EM algorithm
[14, 15]. Experiments show that this framework not only expe-
dites pitch tracker design, but also yields good performance for both
pitch/F1/F2 estimation and voicing decision.
Vowel classification accuracy is crucial for overall VJ perfor-
mance since these categories determine motion direction. Additional
requirements include real-time, consistent, and noise robust perfor-
mance. Vowel classification in the VJ framework differs from con-
ventional phonetic recognition in two ways: First, the vowels are
longer duration than in normal speech. Second, instantaneous classi-
fication is essential for real-time performance. In our system, we uti-
lize posterior probabilities of a discriminatively trained multi-layer
perceptron (MLP) using MFCC features as input. We have also de-
veloped a novel algorithm for real-time adaptation of the MLP and
SVM parameters [12, 13], this increases the accuracy of our VJ clas-
sifier considerably!
We have also found that acceleration has yielded significant im-
provements to VJ performance [11]. Unlike normal mouse accelera-
tion, which adjusts a mapping from a 2-D desktop location to a 2-D
computer screen location, a VJ system must map from vocal tract
articulatory change to positional screen changes. We utilize the idea
of “intentional loudness,” where we normalize energy based on how
a user intends to affect the mouse pointer, and have developed a non-
linear mapping that has shown in user studies to be preferable to no
vocal acceleration.
Our discrete sound recognition module uses a fairly standard
HMM system. We currently use consonant-only patterns for discrete
sounds, like /ch/ and /t/ (sufficient for a 1-button mouse), and we use
a temporal threshold to reject extraneous speech. This not only sig-
nificantly reduces false positives (clicks), but also saves computation
since only pure unvoiced segments of a certain length will trigger the
discrete sound recognition module to start decoding.
3.1. User Study: Comparison of VJ and Eye Tracker
We performed a study comparing a VJ-mouse with a standard eye
tracking mouse. Specifically, we investigated the difference in users’
performance between the Vocal Joystick (VJ) system and the eye
tracker (ET) system.
The eye tracker consisted of a single infrared camera that was
designed to focus on the users dominant eye and to track the move-
ment of the iris by analyzing the reflection of an infrared beam em-
anating from the camera. This particular system required the user’s
head to stay fairly steady, so we utilized a chin rest for the partic-
ipants to rest their chin and reduce the amount of head movement
(something not needed by a VJ-based system). Clicking is per-
formed by dwelling, or staring at the desired point for a fixed amount
of time. The dwelling time threshold is configurable, but we used the
default (0.25 seconds) throughout the experiment.
We recruited 12 participants from the UW Computer Science
department to participate in our experiment. Of the 12, there were
five females and seven males, ranging in age from 21 to 27. Seven
of the participants were native English speakers and the rest of them
were from Europe and Asia. Five participants wore glasses, two
wore contact lenses, and the rest had uncorrected vision. We ex-
posed each participant to two different modalities: VJ and ET. For
each modality, we had the participants perform two tasks: Target
Acquisition task (TA) and the Web Browsing task (WB). The order
of the tasks within each modality was fixed (TA then WB). The par-
ticipants completed both tasks under one modality before moving on
to the other modality. Before starting on any task for each modal-
ity, the participants were given a description of the system they were
about to use, followed by a calibration phase (for VJ, the partici-
pants were asked to vocalize the four vowels for two seconds each;
for ET, the participants were asked to look at a sequence of points
on the screen based on the Eye Tracker calibration software). They
were then given 90 seconds to try out the system on their own to get
familiar with the controls.
All experimental conditions were shown on a 19-inch 1024x768
24-bit color LCD display. The VJ system was running on a Dell
Inspiron 9100 laptop with a 3.2 GHz Intel Pentium IV processor
running the Fedora Core 2 operating system. A head-mounted An-
drea NC-61 microphone was used as the audio input device. The ET
system was running on a HP xw4000 desktop with a 2.4 GHz Intel
Pentium IV processor running Windows XP Service Pack 2. The ET
camera was Eye Response Technologies WAT-902HS model, and the
software was Eye Response Technologies ERICA version
For the TA tasks, we wrote an application that sequentially dis-
plays the starting point and the target for each trial within a maxi-
mized window and tracks the users clicks and mouse movements. A
Firefox browser was used for the WB tasks. The browser was screen
maximized such that the only portion of the screen not displaying
the contents of the web page was the top navigation toolbar, which
was 30 pixels high.
The TA task consisted of sixteen different experimental condi-
tions with one trial each. A trial consisted of, starting at a fixed re-
gion at the center of the screen (a 30 pixel wide square), attempting
to click on a circular target which appears with a randomized size,
distance, and angle from the center region.
The WB task consisted of one trial in which the user was shown
a sequence of web links that they needed to click through and was
told to follow the same links using a particular modality (VJ or ET).
The participants were first guided through the sequence by the ex-
perimenter, and then asked to go through the links themselves to
ensure that they were familiar with the order and the location of
each link. They were also instructed that if they click on a wrong
link, they must click on the browsers back button on their own to
return to the previous page and try again. Once the participant was
familiar with the link sequence, they were asked to navigate through
those links using a particular modality. The time between when the
participant started using the modality and when the participant suc-
cessfully clicked on the last link was recorded, as well as the num-
ber of times they clicked on a wrong link. The sequence of links
consisted of clicking on six links starting from the CNN homepage
www.cnn.com. Most of the links were 15 pixels high and link
widths ranged from 30 to 100 pixels wide, and distances between
links ranged from 45 pixels to 400 pixels, covering directions corre-
sponding roughly to six different approach angles.
The mean task completion speed (inverse time) across all par-
ticipants for each of the 16 conditions across the two modalities is
shown in Figure 3. The higher bars on the graphs indicate faster
performance. The circles represent the target size, distance, and an-
gle relative to the start position (middle square) for all conditions.
The error bars represent the 95th percentile confidence interval (as
with the other error bars in the other figurein this section). The mean
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Fig. 3. Mean task completion “speed” (1/seconds) for the TA task
across modalities.
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TEJV
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Time (sec)
Fig. 4. Web browsing task completion times (sec) across modalities
task completion times (which include any missed-link error recovery
time) for the web browsing task are shown in Figure 4.
Overall, our results suggest that the Vocal Joystick allowed the
users to perform simple TA tasks at a comparable speed as the par-
ticular eye tracker we used, and that for the web browsing task, the
VJ was significantly faster than ET. This is quite encouraging given
that the VJ system is quite new!
3.2. Related Work
There are a number of systems that have used the human voice in
novel ways for controlling mouse movement. We point out, however,
that the Vocal Joystick is conceptually different than the other sys-
tems in several important respects, and this includes both latency and
design. First, VJ overcomes the latency problem in vocal control.
VJ allows the user to make instantaneous directional changes using
one’s voice (e.g., the user can dynamically draw a ”U” or ”L” shape
in one breath). Olwal and Feiner’s system [8] moves the mouse only
after recognizing entire words. In Igarashi’s system [7], one needs
first to specify direction, and then afterwards a sound to move in the
said direction. De Mauro’s system [18] moves the mouse after the
user has finished vocalizing. The VJ, by contrast, has latency (time
between control parameter change in response to a vocal change) on
the order of reaction time (currently, approximately 60 ms), so direc-
tion and other parameters can change during vocalization. The other
key difference from previous work is that VJ is general software in-
frastructure, designed from the outset not only for mouse control,
but also for controlling robotic arms, wheelchairs, normal joystick
signals, etc. A VJ system is customizable, e.g., the vowel-to-space
mapping can be changed by the user. Our software system, more-
over, is generic. It outputs simultaneous control parameters corre-
sponding to vowel quality, pitch, formants (F1/F2), and amplitude
(i.e., we have unused degrees of freedom in the mouse application).
The system can be plugged into either a mouse driver or any other
system.
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... Different systems based on Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis have already been researched for their use by the handicapped community. The STARDUST project aimed to provide oral commands and control of a home environment [1,2]; the Vocal Joystick was designed to provide accessibility to computers by heavily impaired users [3,4]; and, finally, the VIVOCA project created communicative aids able to recreate the speech from a disordered user [5,6]. ...
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