Augmenting Conversations Using Dual–Purpose Speech
Kent Lyons, Christopher Skeels, Thad Starner
Cornelis M. Snoeck, Benjamin A. Wong, Daniel Ashbrook
College of Computing and GVU Center
Georgia Institute of Technology
Atlanta, GA 30332-0280 USA{kent, cskeels, thad, cmsnoeck, bbb, anjiro}@cc.gatech.edu

ABSTRACTIn this paper, we explore the concept of dual–purpose speech:
speech that is socially appropriate in the context of a human–
to–human conversation which also provides meaningful input to a computer. We motivate the use of dual–purpose
speech and explore issues of privacy and technological challenges related to mobile speech recognition. We present
three applications that utilize dual–purpose speech to assist auser in conversational tasks: the Calendar Navigator Agent,
DialogTabs, and Speech Courier. The Calendar Navigator
Agent navigates a user’s calendar based on socially appropriate speech used while scheduling appointments. DialogTabs
allows a user to postpone cognitive processing of conversational material by proving short–term capture of transient information. Finally, Speech Courier allows asynchronous delivery of relevant conversational information to a third party.
Additional Keywords and Phrases: Speech user interfaces, dual–purpose speech, mobile computing
1 INTRODUCTION

Much of our lives is spent communicating with others: a
study of office workers found that 60–85% of their time atwork was spent in interpersonal communication [17]. Increasingly, our interactions are in mobile settings; for two
office workers, Whittaker et al. found that 17% of their total work day was spent in conversations while “roaming” oraway from the desk [27]. In this paper, we present a technique designed to leverage a user’s conversational speech.
Specifically, we are interested in supporting conversationaltasks by utilizing dual–purpose speech. A dual–purpose
speech interaction is one where the speech serves two roles.
First, it is socially appropriate and meaningful in the context of a human–to–human conversation. Second, the speechprovides useful input to a computer. A dual–purpose speech
application can listen to one side of a conversation to provide
beneficial services.
Many office workers have adopted the practice of carrying
personal digital assistants (PDAs) and other mobile computing technology to assist them during conversations. The
computers are used to schedule appointments, take notes, andjot down reminders. Manually entering the information encountered during the conversation is the predominant input
mechanism. The following example illustrates the issue. Alice is trying to schedule a meeting with her manager, Bob.
Italics denote the process of Bob using his PDA:

Alice:
Bob:
Alice:
Bob:

Alice:
Bob:

Bob, can we meet next week?
Bob pulls out his PDA.
Next week you said?
Bob starts the scheduling application.
Yes, how about Monday?
Bob uses his stylus to switch to month view.
Monday, let me check.
He selects next Monday to change to day view.
I’m busy all day Monday.
Bob advances the calendar one day.
How about Tuesday?
Tuesday at one then?
Bob selects the 1:00 entry.
Sounds good. I’ll pencil you in at one.
Bob enters Alice’s name at 1:00 and puts
away his PDA.

This example illustrates some of the current interaction issues with tools used during conversation. Although the information to schedule the appointment was spoken during
the conversation, Bob must still manually enter it into his
computer. Additionally, it is difficult for Bob to participate
in the conversation and navigate through the applications on
his PDA at the same time. Instead, he must put Alice “onhold” while he interacts with his scheduler.
Our proposed technique of utilizing dual–purpose speech allows Bob to converse with Alice while also providing sufficient information for his computer to automatically movethrough his calendar. Although our method uses speech
recognition, our approach does not require Bob to suspend
his conversation with Alice. Instead, our application allows
Bob’s speech to fulfill its traditional conversational role with
Alice while also serving as input to his computer. We explore
this example more thoroughly in Section 4.1.
In the remainder of this paper we discuss more in–depth theidea of dual–purpose speech and explore the issues involved
in mobile speech recognition from the standpoints of technology and privacy. Next, we present three applications which
utilize dual–purpose speech: Calendar Navigator Agent, DialogTabs, and Speech Courier. We then discuss the common dual–purpose speech issues raised by these applications
and describe the common speech infrastructure we have utilized. We then present an evaluation of the Calendar Navigator Agent and finally discuss future work.

2 RELATED WORK

3 DUAL–PURPOSE SPEECH

The concept of machine speech recognition was popularized
by Vannevar Bush in his 1945 Atlantic Monthly article “As
We May Think” [4]. A speech interface was hypothesized
to be faster and more “natural” than typing or writing, andinitial Wizard of Oz experiments by Gould in 1983 on computer assisted dictation supported this hypothesis [9]. Most
public development efforts in the last decade have focused on
dictation or interactive voice response systems (e.g. “Showme the flights from Dallas to Pittsburgh”) [14]. Several researchers have explored the limits of current speech recognition technology and its appropriateness for various interfaces
and situations [20, 31, 16, 7, 19]. Shneiderman provides abrief overview of the issues in his “Limits of Speech Recognition” [22], and Cohen and Oviatt provide a detailed list of
conditions when speech may be advantageous in “The Roleof Voice Input for Human-Machine Communication” [6].

Dual–purpose speech may already be familiar to the reader
from other settings. For example, a lawyer may have her assistant, Alice, in the office while on the telephone with a colleague. Upon agreeing to exchange some information, shemight tell her colleague “My assistant Alice will send you
our new proposal today.” This utterance is dual purpose; it
informs the colleague of the lawyer’s intention and provides
Alice with the specifics needed to fulfil her instructions without further interaction. We explore this scenario with our
Speech Courier application (Section 4.3).

In this paper, we employ many speech interface techniques
described by these authors to constrain our problem of recognizing speech. Our work is also influenced by systems
which forgo speech recognition and store the audio directly,
using other cues such as pen strokes, location, or time of day
for indexing the audio [26, 25, 29, 30, 10]. Such “speech–
as–data” systems are directed at situations when the amountof spoken information is overwhelming, such as attending a
conference. By using similar interface techniques, our applications degrade gracefully despite potential errors with our
speech recognition.
Two applications of interest are Lumiere and the Remembrance Agent. Lumiere models the user and her context toenable applications to provide assistance to the user [12].
Similarly, one use of dual–purpose speech is with applications that act upon the content of a conversation to provide
services to the user. The Remembrance Agent (RA) performs
continuous associative searches based upon the user’s current
document and context [18]. In preliminary investigations ofdual–purpose speech, we explored using the RA with speech
recognition as the data source. While the user could produce
dual–purpose speech, the conjunction proved to be inappropriate as the error rates were too high to produce meaningful
RA results. This initial investigation prompted us to explore
a more constrained domain where we could control the vocabulary to increase speech recognition results.
Work on human–human communication is also relevant toour use of dual–purpose speech. In particular, Speech Acts
Theory states that the act of saying something performs anaction [2, 21]. In a dual–purpose setting, one utterance might
perform two speech acts: one for the conversational partnerand one for the computer. In general it would be difficult
to automatically interpret speech acts with a computer because the computer has limited access to the user’s history
and context, and this information is critical to the meaningof a speech act. Furthermore, people often mean more thanwhat they actually say [21]. As we will show, the scopeof our applications is sufficiently constrained so that we canmake some assumptions about the nature of the speech being
used, and all of our applications use push–to–talk so that theuser segments the machine relevant portions of the speech.

We are extending the concept of dual–purpose speech to be
a computer interaction technique. Consider a problem described in 1998 by the Boston Voice Users Group [8]. One of
the group members, who used a commercial speech recognition package for his everyday work, noticed that it was inconvenient and socially awkward to disengage the system when
guests visited his office. Before he could speak to his guest,
he had to turn off the system by saying “Go to sleep.” Hewould then turn to his visitor, say “Just a second” and remove his headset and earpiece. Eventually this individual
discovered the solution to his problem. Rather than tellingthe system “Go to sleep,” he changed the stop command forthe system to “Just a second.” This modification allowed
his speech to serve a dual purpose: it disabled the speech
recognition system and gracefully informed his guest that hewould be ready to converse shortly. The dual–purpose speechtransformed a socially awkward situation into one in whicha single utterance served two purposes: a command to thecomputer and a polite comment to the guest.
We have developed this technique as a way to enable computer support during conversations. Effective use of speech
as an interaction technique in this domain is challenging.
During a human–to–human conversation it is important thatany speech interaction with a computer fit the flow of the conversation. For instance, there are numerous situations where
it would be socially inappropriate to talk directly to a computer. By using dual–purpose speech, a person can maintainsocially appropriate speech: speech where the language andgrammar used fits the conversation. While it is importantthat the language used is socially appropriate it might not bestrictly “natural.” The user may need to slightly modify her
language to effectively use the application. Even so, with ourdual–purpose speech the resulting conversation still follows
social conventions and sounds “natural” to the conversational
partner. The applications we present in Section 4 utilize thecontent from the user’s side of the conversation and attempt
to minimize disruptions in the flow of conversation.
One notable feature of our applications is that they depend
only on the speech of one person, the user. Many otherprojects involving scheduling recognition tasks assume thatall sides of the conversation are available [24, 3]. Recording other people’s speech without their permission, however,leads to privacy concerns. Also at issue are the limitations ofcurrent speech recognition technology.
With only one side of the conversation available, one might
think that it is infeasible to obtain all of the required information to complete a task such as scheduling. However, theuser can assist the computer by repeating important points
that the other person has stated. People often repeat what another person has said to confirm understanding. It is likelythat the user already repeats much of the critical information,and the conversational partner is unlikely to realize that theuser is repeating any additional information for the benefitof his applications. The example conversation in Section 4.1
reflects this behavior.
3.1 Privacy

A primary concern with speech recognition is the need torecord audio, which can lead to issues with privacy. In mostareas of the United States, recording of conversations withelectronic devices is permissible if at least one participant in
the conversation is aware of the recording. In twelve states,
however, all participants in a conversation must give consent
for recording in most situations.
We have constrained the use of speech in our applications inan effort to preserve privacy. Currently many mobile devices,such as mobile phones, have the ability to record the audio ofconversations around the device. Anecdotally, our colleagues
have found that it is possible to record people’s voices fromacross a room on some mobile phones. Our primary mechanism for avoiding this effect and insuring the privacy ofothers is to use a high quality noise cancelling microphone.
Worn near the user’s mouth, these microphones cancel out
nearly all ambient sounds except for the user’s voice. In our
experience, this greatly reduces the volume of or eliminates
the conversational partner’s voice from the captured audio.
With this technique, our applications utilize only the user’s
side of the conversation.
3.2 Speech Recognition

Limitations of current speech recognition technology makerecognizing meaningful portions of casual conversation very
difficult. Mobility significantly confounds speech recognition, resulting in higher error rates and restricts the types ofdevices and methods that may be used for error correction.
For instance, many speech recognizers have not sufficiently
addressed the varying noise situations that occur during mobile speech. Bursty street traffic noise and microphone noisedue to wind can significantly impact a recognition systemthrough insertion errors.
While recognition systems will continue to improve, someerrors must be expected. A key strategy we employ to reduce the number of errors is push–to–talk. With push–to–talk, the user specifies which parts of the conversation thecomputer should attend to by pressing a button. This greatly
simplifies the speech recognition task. Instead of continuously processing speech, the computer only needs to recognize the portions of a conversation marked by the user. Thesephrases contain higher ratios of known keywords and sentences to out–of–vocabulary words and out–of–grammar sequences. We can further reduce errors by formulating appropriate grammars and vocabularies to be recognized. Phrases
are chosen to cue the applications while simultaneously informing the user’s conversational partner in a socially acceptable manner. While these restrictions are not ideal, they enable us to explore the uses of dual–purpose speech and might
be eased as technology improves.

4 APPLICATIONSIn this section we present three prototype applications that
illustrate our technique of dual–purpose speech. Since many
conversations occur while roaming [27], we built our applications so that they can be used while mobile. These dual–
purpose speech applications reduce the amount of manual input and instead reuse material from the conversation.
The three applications are the Calendar Navigator Agent,
DialogTabs, and Speech Courier. The Calendar Navigator
Agent aids in scheduling. DialogTabs enables a user to augment her short term memory. The third prototype, Speech
Courier, enables a user to alert a non–present third party torelevant material from her conversation.
4.1 The Calendar Navigator Agent

The Calendar Navigator Agent (CNA) is a calendar application that has been augmented to utilize the user’s speech during a social interaction. The CNA automatically navigates aperson’s calendar based on a socially appropriate dialog usedwhile creating an appointment. The goal is to allow user interaction with the calendar that has minimal disruption of thescheduling conversation.
When the Calendar Navigator Agent is started, it shows afamiliar style of scheduling application (Figure 1a). The
graphical interface is similar to common scheduling applications available on PDAs or desktops. As the user proceedswith a conversation, he can hold the “talk” button to run the
speech recognition. The speech fragment is processed by the
speech recognition engine using a limited grammar tailoredto calendaring (for more details, see Section 6.1). Specifickeywords such as “next week” or “Monday” are recognizedby the CNA’s speech recognition engine and used to perform
specific actions. If an error is made and an improper action
is performed, the user can press a single button to undo thelast command.
In Section 1, we described a motivating scenario for our workin which Alice is trying to schedule an appointment with
Bob. We will now revisit that scenario, and show how usingdual–purpose speech eases the conversation for both participants. Bold face text indicates words spoken by Bob while
push–to–talk is active.
The conversation begins with Alice requesting a meetingwith Bob.
Alice:

Bob, can we meet next week?

Bob starts the CNA (Figure 1a) and presses the “talk” key toactivate recording.
Bob:

Next week you said?

Bob releases the “talk” key to stop recording. The CNA recognizes the key words “next week” in the sentence; knowing
the current date, it jumps the display to next week (Figure1b). As this is occurring, Alice is speaking:Alice:

Yes, how about Monday?

Glancing at Monday on the display, Bob quickly sees several
meetings, but it’s unclear for how much of the day he’ll beoccupied.
Bob:

Monday? Let me check.

The CNA recognizes the keyword “Monday,” and switches
the view to a close–up of Monday (Figure 1c). It is now clear
to Bob that Monday is mostly full. Remembering from theweek view that Tuesday seemed clear Bob suggests to Alice:Bob:

I’m busy all day Monday.
How about Tuesday?

The CNA, detecting the keyword “Tuesday,” jumps the viewto the next day (Figure 1d). Bob can see that he has few
appointments. Alice suggests a time.
Alice:

Tuesday at one, then?

Bob sees that one o’clock on Tuesday afternoon is free.
Bob:Sounds good. I’ll pencil you in at one.

The CNA recognizes “one” as a time and creates a new appointment (Figure 1e). Bob may now finish the conversation
with Alice. Afterwards, he can fill in the rest of the relevant information for his meeting at his leisure as our speech
recognition engine is currently not capable of recognizing thenames of people or places.
This conversation is nearly the same as the original; howeverin this scenario, the amount of information that Bob has tomanually enter into the schedule is greatly decreased. Instead, the CNA uses conversational information to navigate
the calendar. Bob’s interaction with his computer is reduced
to using the push–to–talk button, pausing briefly during theconversation to glance at his calendar, and filling in the uncaptured meeting information after the conversation is over.
4.2 DialogTabs

In the previous example, we show how the CNA allows navigation through a calendar. Bob postponed the job of fillingout the details in his scheduler entry until after his conversation was over. A natural extension of the CNA would be tocapture the audio for this portion of the conversation. The
idea of postponement during a conversation is explicitly supported with our next application, DialogTabs.
DialogTabs is designed to help compensate for the limits ofshort–term memory. Unlike other short term audio reminders(such as the Personal Audio Loop [10]) DialogTabs only processes the user’s side of the conversation and uses a push–to–talk button to segment out the relevant portion of a conversation. A small widget, the Dialog Tab, is created to provide
a visual reminder of the recording. After the conversation,
the user can re–listen to the postponed audio and view anattempted speech–to–text translation (Figure 1f).
Imagine that Bob, after finishing setting up his meeting with
Alice, encounters his boss Eve in the hall as she is on her
way to an important meeting. Eve has some information for
Bob: she wants him to call a client and quickly tells him thephone number.
Eve:Bob, please call our client about the new proposal.
They are out of the office; the number is 555-1292.

Rather than open the notepad application on his PDA and try
to write the number or look for a pen and a scrap of paper,
Bob quickly pushes the DialogTabs button and repeats thenumber back to Eve.
Bob:

555-1292. I’ll do it now.

When Bob stops recording, DialogTabs creates an unobtrusive tab on the side of Bob’s screen; as Bob returns to his
phone he can go back and view the tab with the number tomake the call. In addition to recording the phone number,Bob exhibits good social etiquette; by repeating the number
back to Eve, Bob lets Eve know he heard her correctly.
DialogTabs is explicitly designed to make use of dual–purpose
speech. While it could be used as a general short term audio reminder outside of a conversation, using dual–purpose
speech makes it well suited as a conversational aid. Many
conversations are very short and any time spent diverting attention towards a PDA or paper takes away from the conversation. By reducing the interaction to a single button press
and reusing speech from the conversation the cost of the interaction becomes very low.
Visual feedback for each speech segment is generated byshowing a Dialog Tab. As they are created, tabs stack vertically in order of arrival. The most recently created tab is
the tallest, appearing at the top of the stack and covering
twice as much screen space as the next tab. Together thetabs appear as a thin vertical bar at the right edge of the display (Figure 1f). During the course of the day, several tabs
may queue up, but the user does not need to process them
until he has the time and inclination to do so. The stacked
tabs provide a reminder of the information that is waiting forattention, so the user can postpone considering the conversational segments without fear of forgetting them. As each tab
is created, the system attempts to recognize the segments ofspeech recorded for each tab. Hovering the mouse over a tab
displays the recognized text, while clicking a tab brings up
a dialog box showing a visual representation of the recorded
audio along with the text (Figure 1f). The user can click on
portions of the audio or words in the text to hear that segmentof audio.
Creating a grammar for a general purpose DialogTabs application would be very challenging. To address this issue,
we have built several different versions of DialogTabs that
use task–specific grammars. Our first uses the CNA grammar while another uses a grammar designed to parse phone
numbers. However, even in a more general unconstrained
case, DialogTabs is designed to be useful with numerous
recognition errors. An inaccurate transcript can be sufficient to remind the user of the contents of the conversation
fragment, and if not, the user can replay the original audio.
Our graphical interface for the transcript is similar to that of
the SCANMail system [28], which allows users to visuallybrowse voicemail messages.
4.3 Speech Courier

Our final prototype application is Speech Courier. This tool
is designed to relay relevant conversational information to an
absent third party and was inspired by informal observations

(a)

(b)

(c)

(d)

(e)

(f)

Figure 1: (a) The CNA starts and displays the current date. (b) Cued by “next week,” the CNA shows the overview of
Bob’s schedule the following week. (c) The CNA recognizes “Monday” and shows the detail view for that day. (d) The
CNA jumps forward one day when “Tuesday” is recognized. (e) Once the CNA recognizes the time, one o’clock, a new
appointment is created. (f) DialogTabs display unobtrusively on the right side of the display. The pop–up allows the user
to see the transcribed speech and listen to portions of the audio.

of a high level manager and his work routine. Communication and delegation of tasks to the manager’s coworkers
consumes much of his work day. Several times a day whileconversing with a colleague, either face–to–face or on thetelephone, a new task for his assistant is generated. Often his
assistant is present during the conversation waiting for tasks
that might be created.
For example, Eve might say to Bob:
Eve:

Yes Bob, Alice will email you the write–up
for our new proposal.
Bob understands he will get an email.
Alice knows to send the email.

Alice is present during the conversation and Eve’s speech
serves a dual purpose: informing Bob and tasking Alice. Figure 2a depicts this situation. Alice understands the new taskeven though there was not a separate explicit communication
between Eve and Alice. Unfortunately this type of interaction requires Alice to be present for the conversation and limits her ability to do other work. If Alice is not present, Eveneeds to remember at some other point to give her the newtask. As Eve is very busy and often gets distracted by otherwork, she can easily forget to assign the task to Alice. With
the manager we observed this happen on several occasions.
Speech Courier can be used to transform the synchronous
dual–purpose face–to–face speech of this situation to a remote asynchronous communication. Using Speech Courier,a user can easily capture an important part of the conversation and send it to a non–present third party. The user marks

the important points of the conversation using the push–to–talk button as with our other applications. Once the audio
is captured, the speech recognition engine generates a transcript and the audio and transcript are bundled into a package
and sent to the third party recipient via email. In our implementation, a single “assistant” user is configured to receive
the package and they might be used to convey action items,
tasks, reminders, or updates to the non–present person.
Returning to our example, if Alice were not present she
would not overhear her task. Using Speech Courier, Eve cantag and save the relevant portion of her conversation and send
it to Alice. During the conversation with Bob, she uses the
Speech Courier button to record the relevant portion of her
speech.
Eve:

Yes, Bob, Alice will email you the write–up
for our new proposal.
Bob understands he will get an email.
Speech Courier sends the task to Alice.

Speech courier creates a package with the audio and a transcript and automatically sends it to Alice (Figure 2b).
Our speech recognition language model for Speech Courier
is more broad than CNA or DialogTabs, but still rather limited. The speech recognition produces several errors whenwords are not in the vocabulary. The recorded speech would
likely be sufficient to understand the message but the addition of a mostly correct transcript should improve the utilityof the application [28]. Because this information is intended
for another person, the user might wish to correct any er-

AliceIntentional
Unintentional

Alice

Restricted
CNA, DT, SC
N/A

Unrestricted
Co–located assistant
Naive ideal

Table 1: Design matrix of dual–purpose speech. Our
applications are restricted and intentional.
SpeechCourier

Eve

Bob
(a)

Eve

Bob
(b)

Figure 2: (a) When present, Alice can follow the conversation between Eve and Bob waiting for tasks. (b)When Alice is absent, Eve saves relevant portionsof her conversation with Bob using Speech Courier,which then forwards the information to Alice.

rors or add additional comments. Speech Courier provides
the user the ability to edit the recognized text before sending
the package and uses an interface similar to the pop–up of
DialogTabs (Figure 1f). As the package is created when theuser is engaged in conversation, she would likely delay thisinteraction until finished. The editing capability allows thetranscript to serve as a rough draft for a note destined to thethird party.
5 DISCUSSION

Calendar Navigator Agent, DialogTabs and Speech Courier
all use dual–purpose speech to provide support for a user
engaged in conversation. This technique eliminates, postpones, or reduces manual interactions until socially appropriate. While ease and speed of interaction are design considerations in any application, the duration of an interaction
is critical when designing tools for use in conversation. Conversations in the office are quite brief; one study shows theaverage length of a conversation as a mere 113s, while 50%of conversations last less than 38s [27]. Any time spent interacting with a computer can disrupt the flow of conversation.
In the worst case, the user will avoid using the tools altogether.
Our dual–purpose speech applications have the advantage ofa low cost of failure, where failure is many recognition errors on the part of the speech recognition engine. With DialogTabs and Speech Courier, a completely inaccurate transcript will mean only that the user must listen to the entire
clip of speech which can be done after the conversation iscompleted. Imperfect recognition on the part of the Calendar Navigator Agent forces the user to address the error. She
must either repeat the phrase in a socially appropriate way
while avoiding a cascade of errors or revert to manually navigating through the calendar. In the latter case, the rest of
the interaction would be the same as if she had used manual
input the whole time.
Even though our applications only listen to the user’s sideof the conversation to protect the privacy of others, they still
offer beneficial functionality. In the cases where the dual–
purpose speech applications need additional information, theuser can repeat back to her conversational partner. The echo-

ing of key dates in the CNA or repeating a phone number
during a conversation allows the user to both give input tothe computer and also confirm that the message has been
heard and understood properly. Repeating key points is often
already performed when communication channels are pooror the information is particularly important. For instance,
military radio conversations have special protocols to ensure
proper communication [1]. Even though repeating back information for use by the computer may mean a small change
in communication habits, the privacy benefits of only recording the user’s side of the conversation are significant.
5.1 Dual–Purpose Speech Design Space

These three applications highlight important aspects of dual–
purpose speech. The first issue is if the dual–purpose speech
is intentional or unintentional. That is, does the user intentionally speak to both her conversational partner and thecomputer or just to the conversational partner? Closely related is the question of the language used; it can either be restricted or unrestricted. The next issue concerns the intended
recipient of the speech. The recipient can be a computer or aperson, and if it is a computer, it can act upon the speech likethe CNA or only passively record and transcribe the speech.
First we will discuss intentional and restricted dual–purpose
speech. Intentional dual–purpose speech is when the speakerintends for her speech to be directed towards both parties.
Unintentional dual–purpose speech is formulated only for asingle recipient even though the second is listening and acting. Unrestricted language is natural everyday speech withno boundaries, whereas restricted language requires a predefined limited vocabulary (Table 1). All three of our applications are intentional and use restricted language. At thevery least, the user must press the push–to–talk button tosegment her speech. She must also intentionally restrict her
speech to the language model of the three applications. An
example of intentional and unrestricted dual–purpose speech
can be found in scenario that inspired Speech Courier where
Eve talks to Bob while Alice is listening (Figure 2a). Eveis explicitly talking to Bob but also formulating her speechso Alice understands. The case of unintentional restricteddual–purpose speech cannot exist because a speaker can only
restrict her speech intentionally. Lastly, unintentional unrestricted speech would be the least burdensome for the user,
possibly creating a better user experience. Unfortunately current speech recognition requires some restriction in the user’s
language to achieve satisfactory results. Furthermore, it is
not clear if the user’s language would contain enough information to be of use given the implications of Speech Act Theory [2, 21].
The next issue to consider is the intended recipient of thedual–purpose speech information and whether an application acts upon the person’s speech or uses it passively. Di-

alogTabs is an example of a passive application that buffers
the audio and associated transcript for the user. The recipientof Speech Courier package is a non–present third party. The
CNA is an example of an application where the computeris the intended recipient of the speech; the user’s speech ismapped directly to actions performed on the calendar.

6.1 Acoustic and Language Models

The intended recipient changes the impact of speech recognition errors. The CNA operates directly on the speech, and anerror in speech recognition can result in an improper action
with the calendar. If there is an error, the user cannot continue with scheduling until it is addressed. To correct errors,
the user must divert attention away from the conversation,
thereby disrupting the flow and distracting the user from her
primary task. In contrast, if a person is the recipient such aswith Speech Courier or DialogTabs, an error “only” results in
an improper transcription. Errors made in applications suchas these do not interrupt the conversation and can be corrected during a less demanding time (after the conversation
is over). The value of the transcript decreases as the associated errors increase, but the applications still function andremain useful.

In general, acoustic models are separated into speaker dependent and speaker independent models. A speaker dependent model will be more accurate for the particular speakerthat provided the acoustic data, while a speaker independent
model allows many users to be recognized at the cost of reduced overall performance. Given the high barrier of entry for creating acoustic models, we chose to use the freely
available speaker–independent DARPA Resource Management acoustic model2 .

6 IMPLEMENTATION

The implementation of our dual–purpose speech applications
requires a mobile computer such as a wearable or laptop capable of performing speech recognition in near real time andthe user must wear a high quality noise canceling microphone. We also use an input device for push–to–talk anda display for visual feedback. In this section, we discuss how
we met these requirements in building the Calendar Navigator Agent, DialogTabs and Speech Courier.
Utilizing a high quality audio source helps to improve theaccuracy of speech recognition and ensures that recorded
speech is intelligible when played back. To this end, we used
a VXI Talk Pro Max microphone which features active noise
canceling and voice enhancement. The noise canceling feature filters out nearly all ambient sounds except for the user’s
voice, while the voice itself is enhanced by limiting distortion caused by breath pops and other sounds at non–speechfrequencies.
For automatic speech recognition (ASR), we used version
4 of CMU’s Sphinx software [13]. Sphinx 4 is a highly
modular, extensible ASR research system written in Java
that has an architecture which allows for the use of custom
language and acoustic models. Our prototypes consists of
the Sphinx recognition engine, libraries that abstract audio,
speech recognition, and visualization services, and graphical user interfaces to these services. All system components are written in Java 2 Standard Edition and run under
GNU/Linux and Windows XP. Glow1 , an open source Java
calendaring application, was modified for use in the CNA application (Section 4.1). The applications run on a 1.7GHz Intel Pentium IV Mobile CPU laptop and previous implementations of the CNA and DialogTabs have run on an 800MHz
Transmeta–based wearable computer.
1 http://groupware.openoffice.org/glow/

A key issue in building applications that utilize speech recognition is the use of acoustic and language models. Acoustic
models provide information about the low–level features ofspeech such as phonemes, while language models provide
information about pronunciation and grammar.

An important part of our research was constructing an appropriate language model to use in dual–speech situations.
A language model consists of a pronunciation dictionary
and a grammar that specifies how words in that dictionary
combine. A grammar can specify that a sequence such as“How about we meet next week” is highly probable while
the sequence “How a lot of next meet” is not. When a certain conversational task can be assumed such as appointment
scheduling, task specific language can be engineered into thegrammar to reduce processing time and to achieve higher
recognition accuracy. On the other hand, when no specifictask can be assumed, a relaxed grammar must be used which
is necessarily less accurate.
In our implementation of DialogTabs, we chose the limited
task of saving phone numbers. The corresponding language
model represents one extreme along the continuum of grammar constraints. A corpus of eighteen sentences and nineteen
words was constructed. The corpus contains variants of thephrase “So your phone number is. . . ” and the digits zerothrough nine. The probabilistic language model generated
from the corpus contains 19 unigrams, 36 bigrams, and 26
trigrams.
Though still a constrained task, a much more general language model was built for the CNA. The corpus was preparedby observing the language used by participants during a previous study on mobile calendaring [23]. Example phrases
include “How about the day after?” and “Let’s meet October twelfth.” The corpus contains a total of 1007 phrases andthe resulting probabilistic language model contains 121 unigrams, 461 bigrams, and 744 trigrams.
We observed that despite the variation in the language usedin the calendaring corpus, there is little variation in the intent of the language. We identified three semantically distinct
units that could be leveraged for calendar navigation. These
are the initial check of a certain date, the subsequent accessof other dates when the initial check fails (e.g. the user has
a previous engagement), and the final act of confirming theappointment. After recognition is performed on a sentence,
keyword matching is applied to determine which of the threeactions is intended. For example, finding “March” and find2 http://www.ldc.upenn.edu/Catalog/ing “20th” would signal the check of “March 20th,” even if
the spoken sentence was “let’s meet in March...how about
the 20th?”. This keyword–to–intention mapping helps the
Calendar Navigator Agent be more flexible in its recognition
especially if the user strays outside the language model. This
technique in turn reduces the effect of recognition errors andhelps to avoid the cost arising from incorrect navigation.
In contrast to the other applications, a more general purpose
grammar was used as the starting point for Speech Courier’s
language model. This was done to explore the use of unconstrained speech recognition in conversational situations thatare hard to formulate. Our assumption was that any language
model we could construct would perform poorly in an arbitrary situation not accounted for by the model. Our approach
was to build a base model that could be iteratively extended
according to personal experience, informal observations andfuture formal usage studies. The base corpus includes one
thousand common words as well as the scheduling scenario
corpus identified in the CNA language model for a total of2042 phrases and 1050 words. The probabilistic language
model also contains 2437 bigrams, and 1779 trigrams.
7 EVALUATION

We conducted a preliminary study of the Calendar Navigator Agent to investigate its effectiveness for scheduling appointments and for ease of use. Specifically we are interested in the effectiveness of our speech recognition, the easeof use of push–to–talk, and the users’ ability to employ the
restricted grammar. We focused on the CNA because speech
recognition errors are the most critical in this application of
our three and scheduling allows us to explore dual–purpose
speech with a straightforward and realistic task for users.
7.1 ProcedureThree people from our laboratory used the CNA for thisstudy. Everyone had a passing knowledge of dual–purpose
speech and our applications before the study; however, no
one had any experience with our prototypes. The trials lasted
60–90 minutes for each person. The CNA ran on a laptop at
a desk, and the laptop’s screen displayed the application.
The study consists of four parts: a baseline evaluation of
speech recognition, a demonstration of the CNA, training
in two phases, and finally a test with scheduling appointments using dual–purpose speech. These steps are designed
to gradually introduce the users to the language and abilitiesof the CNA. After the experiment, we administered a questionnaire and conducted an interview.
First using a testing application, we obtain a baseline of eachuser’s speech recognition rates for the language of the CNA.
Each user reads through 20 sentences used by the CNA. For
each sentence, the subject uses push–to–talk and speaks thepresented phrase. If there is an error, they repeat the phrase
until it is correct.
The researcher next demonstrates the CNA. He navigates andschedules two appointments using speech. The user is instructed to listen to the speech and watch the resulting actions
taken by the CNA.
Next are the two training phases designed to instruct the users

Accuracy
Correct

A
79.9%
88.9%

B
97.5%
100%

C
83.6%
91.0%

Mean
87.0%
93.3%

Table 2: Word level percent accuracy and percent correct for three users.

on the association between the speech used for the CNA andthe actions performed in the calendar. Users schedule two
appointments per training phase in a sequence of steps. Eachstep represents one turn of the user’s dialog. Part one of the
training is the prompted phase. At each step of this phase,the researcher explains the possible actions that can be takenfrom the current state in the CNA. The researcher then gives
the user a phrase to speak, asks her to repeat it to ensure
she understands what to say, and the user speaks the phrase
to the CNA. The second training phase is the user generated phase. As in the previous phase, the user schedules
two appointments step–by–step. However instead of being
prompted with what to say, the user is given a more generalgoal and asked to generate a phrase to use with the CNA.
Once the participant generates a correct phrase, she uses itwith the CNA.
The last portion of the experiment is the test phase designed
to mimic appointment scheduling conversations. The user
is asked to participate in nine scheduling dialogs with theresearcher. Using the information in the CNA calendar, theuser responds to calendaring requests made by the researcher
or initiates a dialog given a high–level goal (e.g. “schedule
an appointment next week”).
At the conclusion of the experiment, we administered a questionnaire composed of 12 Likert scale questions and used theanswers as a basis for a semi-structured interview.
7.2 Results

While limited in scope, the results from our study are promising. Table 2 shows the word–level recognition rates for our
three users taken from our initial speech recognition baseline phase of the experiment. Percent accuracy is defined as
N −D−S−I
× 100% and percent correct as N −D−S
× 100%
N
N
where N is the total number of words, D is the number ofdeletions, S the number of substitutions and I the numberof insertions. Overall, the mean accuracy for the group is
87.0%, while the percent correct is 93.3%. While better
recognition rates would improve our application, one user
performed very well achieving 100% correctness and 97.5%
accuracy on our 20 phrases. This result indicates that with
an improved or adapted acoustic model, we might be able toenhance our overall recognition rates.
While the word–level speech recognition rates provide an
overall sense of the performance of the application, the actions performed by the CNA are more important. For thetesting portion of the study, phrases were successfully recognized and acted upon by the CNA without errors 80.2% of
the time. Furthermore, each task in the scheduling dialog wascompleted with at most one recognition error 97.8% of thetime. This result implies that uttering the phrase again seems
to be effective for the CNA. Our current language model isvery limited and was not designed to enable socially gracefulcorrection of errors through speech. For our experiment, theuser was asked to repeat a phrase until the CNA performed
the correct action. Given this result, we are exploring ways
to modify our language model so that a user can repeat orrephrase what she said. This ability would enable the computer to try again, while at the same time minimizing any disruption in the flow of the conversation. Most of the speech
recognition errors in our study resulted in no action taken by
the CNA as opposed to the incorrect action. It is possiblethat using a slightly more intelligent algorithm to interpretthe speech might increase the ability of the CNA to performthe correct action when speech recognition errors are made.
The questionnaire and interviews provide additional insight.
Our users quickly accommodated to using push–to–talk andrated it as fairly easy to use during the scheduling conversation. Our users thought that the language for schedulingwith the CNA was fairly acceptable and socially appropriate. Even with the training given, the users indicated that
language generation is the hardest part of using the CNA.
This issue was demonstrated most clearly when the user initiates the scheduling dialog and cannot simply respond tothe conversational partner. The users also realized their own
limitations, and this quote is typical: “This app. would bereally useful given more training.” Ideally, the use of dual–
purpose speech should be much more effortless. Our results
imply that the users needed more training to become experts
in generating the needed speech during conversational situations. We might also attempt to make applications adaptive
so that the user’s speech can more closely match her natural
language. Even with the current limitations, all three users
were enthusiastic about the CNA application and agreed using conversations and dual–purpose speech as a means toschedule appointments would be useful.
8 FUTURE WORK

There are several areas we would like to explore in the future. First, we would like to further examine ways to reduce
the number of recognition errors. By creating better acoustic
and language models which better mimic the everyday settings our applications are designed for, we believe that higher
recognition accuracy can be achieved. One option is to use
speaker–dependent speech recognition. While this would require a great deal of time from the user to properly train
the recognition engine, the increased accuracy and long term
benefits might justify the cost. We are also exploring otherspeech recognition engines which might have better models.
Codifying the language used in everyday situations into appropriate vocabularies and grammars is an interesting research opportunity. The results must reduce the perplexityof the speech recognition problem while maintaining the socially appropriate patterns of natural dialog. One can imagine an effort similar to the DARPA Airline Travel Information Service (ATIS) task where researchers try to capture the“natural” vocabulary and grammar related to a specific task
and then create a system that allows very flexible interaction
while still being specifically tuned to the task [11, 14, 15].
As discussed previously, due to privacy concerns we currently only use one side of a conversation. One interesting
possibility for overcoming this limitation while preserving

privacy is for the participants of a conversation to share theirone–sided recordings with the other participants, allowing
the reconstruction of the entire conversation. Privacy of theshared conversation fragments would be a great concern andwould have to be considered carefully. This goal could be accomplished automatically, by providing some sort of authentication between users’ devices, and by either time–stamping
recordings or by looking at turn–taking behavior [5].
The results from our initial study are encouraging and weare interested in exploring the usability and usefulness of thedual–purpose speech technique in more detail. Of particular interest is the relative costs of using dual–purpose speechin terms of time and cognitive load compared to more traditional mobile devices and interface techniques. We are interested in examining the benefits and drawbacks of using push–
to–talk and our currently limited grammars while controllingfor speech recognition errors and exploring the design spaceof dual–purpose speech more thoroughly.
Finally, we would like to deploy our dual–purpose speech
applications for long term use in real–world situations and
we are interested in exploring the usefulness of the CNA, DialogTabs and Speech Courier in their current limited forms.
We are in the process of porting the software to our wearable computers and are exploring the possibility of using the
new generation of high–end PDAs, such as the Sharp Zaurus
SL-6000, as platforms for these applications.
9 CONCLUSIONS

We introduced the concept of a dual–purpose speech interaction: socially appropriate speech that provides meaningful input to a computer. We show that dual–purpose speech
can be employed by applications to augment conversations.
Our three applications, the Calendar Navigator Agent, DialogTabs, and Speech Courier, explored this design space,
and we identified three aspects of dual–purpose speech: restricted language, intentional use of speech, and intended recipient. We discussed issues of designing interactions based
only on the user’s speech to ensure privacy and robustness inthe presence of speech recognition errors. With future improvements to speech recognition, we expect dual–purpose
speech to become more widely applicable for mobile computing.
ACKNOWLEDGMENTS

This material is supported in part by the NIDRR Wireless
RERC and NSF Career Grant #0093291.
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