Speech Recognition Market
S-89.3680 Speech Recognition Seminar
First name
Family name
Date
Guzman
Alvarez
18/12/2014
Speech Recognition Market
1. Introduction
The aim of this work is to give an overview of what the status of speech recognition is from the
commercial point of view, and try to follow the events that have driven its commercial development
throughout the years.
In the first place, the progression of the market of speech recognition systems is reviewed by
looking at its main milestones: from the first commercial system to the revolution of the cloud.
The next section gives a brief overview of the current status of the market based on financial data of
some of the main players (Nuance, Microsoft, Google, Apple) as well as on other source’s analysis
and prediction of its future.
Finally, how those companies tackle and use speech recognition studied. In addition, a brief Q&A
with the Finnish company Lingsoft is included, as an interesting example of “small” but successful
company that is involved in speech recognition and closely related to Aalto University.
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Speech Recognition Market
2. History
2.1. Early stages and first commercial ASR product
Research activity on speech recognition started in the decade of 1950, with developments by Bell
Laboratories based on previous advances on the field on phonetics, such as the VODER; in 1952
they had a system that could recognize single digits from a single speaker. Other advances
followed, from institutions such as the Massachusets Institute of Technology, RCA Laboratories or
Kyoto University, who designed systems that recognized from basic units of speech up to few
whole words, and that already applied the concepts of non-uniform time scale and statistical syntax
[1].
The first commercial automatic speech recognition (ASR) product was the VIP-100 by Threshold
Technology Inc., first sold in 1972 [2]. It consisted of a hardware preprocessor, a feature extractor
and a classifier function performed by dedicated hardware running in a “minicomputer”. The user
chose the vocabulary and trained the system with 10 utterances of each word, which the recognizer
compared to the input in order to determine which word was being spoken. The system allowed for
rates of up to 125 words per minute, and achieved accuracies of 98.5 % for a 35-word vocabulary in
a noisy environment, or 99.5 % for a 15-word vocabulary in a quiet environment. [2]
2.2. DARPA
Although only around 100 units of the VIP-100 were sold (mainly for industrial activities such as
material handling and quality control) [2], it had a major role in the history of speech recognition,
since it influenced the Defense Advanced Research Projects Agency (DARPA) to fund the Speech
Understanding Research (SUR) project in order to develop speech recognition technology with
military purposes. Its goals, taken from [3], were: “Accept connected speech, from many
cooperative speakers, in a quiet room, using a good microphone, with slight tuning/speaker,
accepting 1000 words, using an artificial syntax, in a constraining task, yielding < 10% semantic
error, in a few times real time, on a 100 MIPS machine.”
Among the results of the SUR program was a recognizer by the Carnegie Mellon University
(CMU), called Harpy, which could recognize more than 1000 words and was the first to use the
concepts of graph search and finite-state networks [4].
Also from the CMU was the Hearsay-II system, which performed several analysis in parallel over
the data in order to obtain different types of evidence, that were combined to obtain a hypothesis.
This idea, which consisted effectively on using a blackboard system for speech recognition, was
novel and unique in the field. [1]
Bolt Beranek and Newman (BBN) created the Hear What I Mean (HWIM) system, which
introduced word verification tests (at the parametric level), the concept of lexical decoding
networks, and a lattice of possible hypotheses that reduced segmentation ambiguity [3].
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Speech Recognition Market
Another system worth mentioning, created in 1975 by James and Janet Baker, is DRAGON [5],
which will be discussed in a subsequent section.
2.3. Research in IBM and AT&T Bell Laboratories
Besides the activities funded by DARPA, IBM and AT&T invested heavily in speech recognition
research, each company following a different approach.
AT&T wanted a system that would allow carrying out certain tasks such as routing calls or dialing
automatically by asking the user to speak the parameters; this implied recognizing a small set of
mostly single words, but at the same time a speaker-independent and robust system was needed,
since it would get input from people from across the whole country. It led to the development of
templates and statistical models for sound reference patterns, and to the concept of spectral distance
measures. They also introduced keyword spotting, in order to detect key utterances irrespective to
the preceding or following speech, which allowed to detect answers spoken in natural language: for
example, to the question “How old are you?” the keywords would be numbers, and the system
would recognize both “twenty-four” or “I am twenty-four years old” as valid answers. [4]
On the contrary, the system from IBM was aimed at being an alternative form of general text input
to other systems, a “voice typewriter”. Thus, the target was to have a large vocabulary and make as
few mistakes as possible. This led to the development of a system called “Tangora”, that used the
concepts of n-grams, language models and found the maximum-likelihood sentence for the input
speech. This system needed a training period for each new speaker in order to adapt the models to
his/her way of speaking. [3]
Neither of these efforts resulted in commercial successes, but settled the grounds for further
research on speech recognition.
2.4. DRAGON, Dragon Systems, NaturallySpeaking
This section summarizes the history of DRAGON and Dragon Systems, and has been sourced
mostly from [6] unless noted otherwise. As mentioned before, one of the notable systems born
under DARPA’s SUR framework was DRAGON. It was conceived by Janet and James Baker, a
couple of doctoral students at the CMU during the time the SUR program was being carried on.
They pioneered the application of the Hidden Markov Models to speech recognition [6], but their
description of the DRAGON’s main features did not stress that point, reading instead: “delayed
decisions, generative form of model, hierarchical system, integrated representation, general
theoretical framework” [5].
After graduating from the CMU in 1975 they joined IBM’s research center, where they developed a
system with a vocabulary of 1000 words that took one hour to decode a single sentence of
continuous-speech. However, IBM did not favor getting simpler-but-usable products to the market,
which the Bakers saw as a more positive approach. For this reason, they started working for Verbex
in 1979, company that was selling a recognizer of digits over phone connections; their aim was to
improve the system until continuous speech recognition was possible.
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Speech Recognition Market
In 1982, when Verbex stopped their speech recognition activities, James and Janet founded their
own company, called “Dragon Systems”. During the first decade they produced several successful
custom discrete-recognition products, including the recognizer used by the world’s first personal
computers that let users execute commands via voice instructions, made by Apricot Computers PLC
[7]. In 1990 they released DragonDictate, which allowed controlling any computer and dictating
text, and was successful among the disabled as an alternative to the keyboard.
Still, the general market did not adopt speech recognition solutions, as all of them were discrete
(required a pause between words) and thus not comfortable for natural speech. In 1994, the Bakers
predicted that the computing capabilities of general-purpose PCs would allow continuous speech
recognition within a few years, and restructured their company in order to be ready when the time
came. In fact, when in 1997 they released the first version of NaturallySpeaking, it became an
immediate success. Although IBM counterattacked some months later, selling their “ViaVoice”
product at a lower price, which produced similar results, Dragon System’s technology has since
then dominated the market until now.
However, in the year 2000 the company was acquired by the corporation Lernout & Hauspie, which
went bankrupt after just a few months [8]. The rights of NaturallySpeaking and its speech
recognition technology were put on sale, and the Bakers could not compete with Scansoft (later
Nuance Communications) in order to buy them back. The Bakers not only lost a handful of money,
but the technology they had been working on for more than 25 years, stating that “part of them
died” after the sale [9]. James Baker is today a Distinguished Professor at the CMU [10], and most
likely an Apple employee (see section 3.5).
2.5. Scansoft, Nuance, Nuance Communications
The current leader in the speech recognition market, Nuance Communications, was born in 2005
from the merge of Scansoft and Nuance [11]. In this section, the history of both former companies
and that of the resulting corporation is briefly reviewed.
2.5.1. Scansoft
Scansoft had its origins in Kurzweil Computer Products Inc., founded in 1974, which specialized in
Optical Character Recognition (OCR) products. In 1980, Kurzweil was bought by Xerox Inc.,
which ran it as Xerox Imaging Solutions (XIS), and later as Scansoft. In 1999, Visioneer, a
company that developed scanner hardware and software, bought Scansoft from Xerox, but
continued to use the same commercial name.
In 2001 Scansoft made a key acquisition, buying the speech technology assets of Lernout and
Hauspie [12], after a financial turmoil described in the previous section. A major part of these assets
were the former technology from Dragon Systems, although Lernout and Hauspie had previously
acquired other companies involved in speech as well. This operation was considered a “bargain”,
taking into account that while Dragon Systems was sold to L&H for $580 M, Scansoft bought the
technology for a total price of approximately $40 M [12].
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Speech Recognition Market
The acquisition meant that Scansoft moved into the speech recognition market, and started
competing with Nuance. In 2003, Scansoft bought SpeechWorks, a direct competitor in the field of
speech recognition, in a $132 M-deal [13], becoming a global player in the market.
During the rest of 2003, 2004 and the beginning of 2005 Scansoft continued to acquire smaller
speech-related companies, which contributed to increase their market share and product portfolio.
2.5.2. Nuance
Nuance was born in 1994 as a spin-off of the Speech Technology and Research (STAR) laboratory
of the Stanford Research Institute (SRI), with the purpose of commercializing the solutions that the
institute had developed for the US Government [14]. The first commercial product was released in
1996, which consisted in a call center automation solution based on speech recognition.
Through the following years they became the global leader in the market of speech recognition
solutions, selling mainly a platform on top of which other vendors could build applications. This
platform featured Speaker-Independent Natural Language Speech Recognition (SI-NLSR), which at
the time was a key advantage over their competitors [15]. The difference between Nuance and
Dragon Systems was that while the former sold a speaker-independent recognizer that other
companies could use in their products, generally involving telephone network operators, the latter
sold end solutions, either personalized for a specific customer or to the general consumer market.
Nuance only acquired the company SpeechFront in the year 2000 [15], continuing to compete with
Philips and SpeechWorks [16] [17].
2.5.3. Nuance Communications
As stated before, Nuance Communications was born in 2005, when Scansoft acquired Nuance, and
started to operate under the new name, which was perhaps more adequate given the fact that after
Scansoft’s latest acquisitions their focus was clearly on the speech recognition market.
Nuance Communications started from a position of leadership both in the consumer market, selling
mainly the NaturallySpeaking product and in the enterprise market, having the portfolio of all the
previously acquired companies.
During the following years, until today, the strategy of Nuance has been to control the speech
recognition market and expand their range of products by taking over potential competitors and
startups. In Section 3.2, an overview of the products that Nuance offers currently is given.
2.6. Siri
Although the word “Siri” is nowadays associated with the digital personal assistant application
present in Apple smartphones, it was initially a company founded in 2007 by Adam Cheyer and
Dag Kittlausas as a spin-off from the Stanford Research Institute [18]. During the previous years,
the SRI had participated in an artificial intelligence (AI) research project called “Cognitive
Assistant that Learns and Organizes” (CALO); this project was in turn part of the “Personalized
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Speech Recognition Market
Assistant that Learns” (PAL) program funded by the DARPA jointly with the Swiss institute of
technology (EPFL), which was the largest (known) AI project in the history of the U.S. [19].
After the foundation, Siri spent around three years developing their product until February 2010,
when they released the “Siri” application in Apple’s App Store, available to the iPhone smartphones
[20]. The groundbreaking feature was the ability to understand natural language, adapting to that of
each user, as well as integration with other services that allowed returning a non-generic response;
for example, if a user asked about the weather, instead of recognizing the question and querying a
search provider, the application would connect to a weather services provider and display the
information directly.
Specialized publications recognized the breakthrough character of the application (for example, in
[21], the author states “this is the future of the web”), and so did Apple Inc., who purchased the
company a few months after the initial release (the operation was made public in April 2010 [22]).
As a result, Siri stopped developing the initial application, canceling their plans to make it available
in other platforms, and worked with Apple to integrate the functionality into the iPhone’s operating
system.
In October 2011 Siri was released as a feature of the iPhone 4S, being the protagonist during the
presentation of the phone to the public. Since then the interest spread globally, and it became clear
for Apple’s competitors that their products needed similar features.
Siri as a feature of the iPhone was a clear breakthrough in speech recognition, not because of the
technology, which was developed years before, but because it started the interest of the general
public in the field, which in turn resulted in bigger research efforts and investments. As an example,
a quote from [23]: “Todd Mozer, the CEO of a company that works with device manufacturers to
create voice interfaces called Sensory, remembers the attitude toward speech interfaces seeming to
change overnight. "You wouldn’t believe the number of companies that suddenly were knocking on
our doors and saying, ‘we want to add speech recognition, we don’t exactly know how, but we know
we need to do it now,’" he says”.
2.7. Cloud
Although the concept behind the term “cloud computing” started to form in the 1950s, and it applies
to general computing, it meant a true breakthrough for speech recognition when it became a feasible
and known enabler. From this point of view the former Siri application was a pioneer in using the
cloud to perform speech recognition.
The main idea behind cloud computing is to run tasks in servers whose physical location is
unknown, using the Internet to send the input from a client towards these servers (the cloud) and to
send the result of the task back to the client.
In the case of speech recognition this is particularly useful, since the use of advanced methods and
models requires heavy computation. In the past, a compromise solution needed to be taken in order
to balance the available processing power with the desirable recognizer quality. Using cloud
computing, the end user needs only a device capable of recording his/her voice and sending
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Speech Recognition Market
fragments to the servers of the speech recognition provider. The recognition is run in powerful
machines that can deliver results almost in real time, and the resulting text is sent back to the user
device, which can use it appropriately. This effectively enables any device that can connect to the
Internet to recognize speech using state-of-the-art systems, eliminating the processing power
limitation.
On top of this, the use of cloud computing allows for small developers to implement speech
recognition in their applications without having to develop the recognizer themselves. For example,
Nuance offers the Dragon Mobile software development kit (SDK), which allows mobile
applications to use Nuance’s speech recognition engine, using different pricing schemes [24].
Giving access to these technologies to every developer who wants to use them will promote new
ways of taking advantage of speech recognition, which in turn will drive the market and promote
faster research and development in the field. In addition, according to ABI Research (see [25]),
these solutions are a considerable factor influencing the growth that mobile speech recognition is
currently experiencing.
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Speech Recognition Market
3. Current status
This section provides an overview of the current situation of the speech recognition market. First,
its approximate size and distribution are studied, as well as the financial evolution of four of the
most active companies in the field. Then, the approach of these companies towards speech
recognition is very briefly looked at, mainly by means of their current lines of products that include
speech recognition. Last, the contents of a “question and answer” session with a manager of
Lingsoft Oy (a language management Finnish company) are reproduced, which give a good insight
in the role of speech recognition in the company.
3.1. Market size, evolution
According to BCC Research [26], the voice recognition market had a size of $47 billion in 2011,
$53 billion in 2012, and will reach $113 billion in 2017, which means a total compound annual
growth rate of 16.2 %. The distribution between consumer, enterprise and health care sections is
shown in Figure 1; it is clear that the consumer market will grow the fastest, whereas no significant
changes are predicted for the medical field. This could be reflecting the increasing interest from the
end users who want to use voice-enabled applications in their smart devices, which promotes the
development of such programs both for the consumer and the enterprise markets.
Figure 1. Distribution of the speech recognition market in 2011, 2012 and 2017 (prediction) [26] Some of the most important companies that either sell or use speech-recognition related services or
devices are at the moment Nuance, Microsoft, Google and Apple. The approach of each of these
towards speech recognition is summarized in the following sections; however, in order to give a
general overview of the market status from a different point of view, the evolution of the shares
price of these four companies during the last 10 years has been collected in Figure 2.
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Speech Recognition Market
Figure 2 Price evolution of the shares of Nuance, Apple, Microsoft and Google in the last 10 years [Combined data from Yahoo Finance] Rather than studying the details of each graph, the conclusion to be drawn from the general picture
is that all of the companies have a history of growth, and it is safe to predict that the same trend will
continue, at least in the upcoming years. Since these are the companies that drive the largest part of
the speech recognition market, it can be expected that it will continue growing in the following
years, which agrees with the previously mentioned prediction by BCC Research [26].
3.2. Nuance Communications
Nuance is the global leader in the speech recognition market; this is a widely known and accepted
fact, but no direct sources have been found (a statement from the own company can be read from
some of their job offers, such as [27]). This leadership position was already present in the roots of
the company, with Scansoft and Nuance being leaders in their respective markets prior to the
merge, and has been maintained through the years.
Nuance uses an aggressive competing strategy, which consists on acquiring well-established
companies that are already competitors, start-ups that might be in the future, or companies who are
already leaders in different markets they plan to get into. In this way, they can both control the
competition and continuously extend their product offering. A detailed list of the companies
acquired by Nuance Communications can be found in [15].
The majority of Nuance’s benefits come from the enterprise market, in which they sell or license
their recognition engines to other companies, and from the health care market, where they offer
ready-made solutions that can be used directly by the institutions. In the consumer market they sell
the Dragon recognizer for personal computers, a personal assistant called “Dragon Asssistant” (Sirilike) for personal computers, and other products not related to speech recognition [28].
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Speech Recognition Market
3.3. Google
Google addresses speech recognition as a philanthropic issue, where their goals are the following
[29]:
− To make speaking to your phones and computers ubiquitous and seamless.
− To help make videos on the web accessible and searchable.
Furthermore, also in [29] it is stated that users diversity is a “huge commitment”, imagining an ideal
situation where every person in the world can use a speech recognizer. Quoting the mentioned text:
“The payoff is immense: imagine making every lecture on the web accessible to every language;
this is the kind of impact we are striving for”.
Google has a unique position that allows for collection of immense amount of data, being the global
search provider leader, providing the default speech recognition engine for Android phones, and
owning the leading video-hosting website “Youtube”. Their research is consequently heavily
focused on finding optimal ways of treating the data, from the points of view of both efficiency and
output (improved models and methods).
They don’t sell to the general public any products that involve speech recognition. Rather, it is
included in the Android operating system and the Chrome browser.
3.4. Microsoft
Even though not as actively as specialized companies, Microsoft has traditionally conducted heavy
research in speech recognition [30]. This continues today, when they have an extensive team of
researchers exclusively dedicated to speech recognition, working in a variety of topics that can be
checked from [31].
They have made possible speech recognition in their main product (the Windows operating System)
since 1995, when the Microsoft Speech Application Programming Interface (SAPI) was released.
Apart from the operating system, they set an important milestone being the first company to use
deep neural networks (DNN) in a commercial product, the Microsoft Audio Video Indexing
Service.
Nowadays, their mainstream products using speech recognition are the Windows operating system,
Cortana (the virtual assistant present in smartphones with Windows Phone), and Skype, which with
the new service “Skype Translate” is setting a new milestone, allowing to recognize, translate, and
synthetize the words of one party in the language of the other, and vice-versa, in almost real time.
3.5. Apple
Given the secrecy Apple maintains about their company and processes, it is not possible to be
certain about how involved they are in speech recognition. Some sources maintain that they made
an important investment prior to including it in their operating system (Mac OS, later OSX) for the
first time, in 1993 [32]; however, the lack of popularity of speech recognition in personal computers
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Speech Recognition Market
during the last decade, together with the fact that they used a recognizer provided by Nuance for
Siri [33], suggest that at least in these last years they have not been very active in the field.
Nowadays the situation might be changing, most likely due to the increased interest of the
customers in voice interfaces; besides many of the customary Apple-related “rumors” about Apple
hiring engineers from Nuance, in the end of 2013 they acquired Novauris [34], a company founded
by the James Baker (co-founder of Dragon Systems, see section 2.4) in 2001 [35].
3.6. Lingsoft
Lingsoft Oy is a relatively small Finnish company specialized in language management, which uses
speech recognition mainly internally (as a part of processes that they sell to other companies) or as a
part of a “wider” product. In order to get an idea of how speech recognition is managed at a smaller
scale, a set of questions were asked to Dr. Vesa Siivola, who is a Development Manager at
Lingsoft, which have been reproduced below:
− Strategy, role of speech recognition in Lingsoft
+ “Our strategy is to help the customer improve their language processes. Sometimes, a
speech recognizer will help to improve some part of the process and thus ends up being a
part of the solution that is offered”
− Common products or customized projects?
+ “All of our current projects involve customization for the client”
− Impact of speech recognition
+ “Most speech recognition clients that we have are using grammar (command) speech
recognition at phone portals (eg., Tallink, Palvelualojen Ammattiliitto, Lindorff).
Continuous speech recognition is currently used mostly by our internal language services
(subtitling of Finnish programs for YLE, transcription of medical patient dictations)”.
− In which fields do you think the demand for improving speech recognition is at the moment?
+ “Since our business revolves around processes, we tend to see cases where there is a high
volume of similar speech transcription tasks and improving the efficiency of the whole
process is the key. Thus, it tends to be big organizations like YLE and hospitals. We have not
concentrated on consumer space”.
− Any special considerations applying speech recognition to the medical field?
+ “For the basic speech recognition engine, it does not differ terribly from any other
customization case. The protection and laws regarding patient medical records pose extra
challenges for getting the corpora required for model customization, though”
− Competitors in the field of speech recognition?
+ “Nuance”
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Speech Recognition Market
4. Conclusion
It can be observed that during roughly 40 years of history of speech recognition, there has been a
clear shift in the interests that drive the research. In the beginning the interest was purely academic;
then the governments realized the potential (for example, DARPA and the SUR program); and at
the same time private companies started to get involved. However, until the beginning of 90’s
speech recognition was mostly a research topic, rather than a commercial product. Afterwards a
slow move into the consumer market started to happen, moved by software solutions for personal
computers, and it “exploded” in 2011 with the introduction of an actually usable voice interface in a
phone. Nowadays the end consumers constitute the largest part of the speech recognition market.
From the point of view of the author, what has been seen until now in terms of voice interaction
with machines is just the tip of the iceberg: using their voice is the natural way most humans
communicate, thus it is logical that as more flexible and reliable systems are developed a larger
amount of people will start to use them; that will lead to more development efforts, and in this way
an always-faster cycle will be created which will bring great advances and even possibly push the
current keyboards into the background.
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Speech Recognition Market
5. Bibliography
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Speech Recognition Market
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Speech Recognition Market
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