The R&D Tax Credit Aspects of Voice-Activated Software



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Voice-Activated-Software
        “There have been more improvements in speech recognition over the past three years than there have been over the past 30 years combined”.  This statement by Tim Turtle, CEO of smart voice start-up Expect Labs, points to the fact that recent technological advancements are revolutionizing the way humans and computers interact. The present article will assess the rise of a new generation of voice-activated applications as well as the wealth of opportunities they bring about. It will also discuss how R&D tax credits can help companies succeed in the emerging voice-driven world.


The R&D Tax Credit

        Enacted in 1981, the Federal Research and Development (R&D) Tax Credit allows a credit of up to 13 percent of eligible spending for new and improved products and processes. Qualified research must meet the following four criteria:

  • New or improved products, processes, or software
  • Technological in nature
  • Elimination of uncertainty
  • Process of experimentation

        Eligible costs include employee wages, cost of supplies, cost of testing, contract research expenses, and costs associated with developing a patent.  On December 18, 2015, President Obama signed the bill making the R&D Tax Credit permanent.  Beginning in 2016, the R&D credit can be used to offset Alternative Minimum tax and startup businesses can utilize the credit against payroll taxes


Voice-Activated Technology

        Voice and speech recognition can be generally defined as the “the ability to convert and decode human voice into speech easily understood by a computer.”  In other words, it enables machines to transform natural language into actionable data.

        There is, however, an important distinction between the two categories. While voice, or speaker recognition, allows for identity verification and therefore focuses on who is speaking, speech recognition is used for issuing operating commands, with focus on what is being said.

        Recent leaps in voice-driven technology were made possible by machine learning capabilities and cognitive computing technology along with unprecedented access to massive amounts of data. Even though advancements so far are beyond impressive, projections for the future are even more exciting.

        Data from artificial intelligence company MindMeld shows that while voice search traffic was negligible before 2015, it currently represents 10 percent of all search traffic. By 2020, 200 billion voice
search traffic was negligible before 2015, it currently represents 10 percent of all search traffic. By 2020, 200 billion voice searches will take place every month.

        According to a recent report by research firm Tractica, the voice and speech recognition market will experience a 40 percent compound annual growth rate between 2015 and 2024, rising from $249 million to $5.1 billion. The report also predicts that licenses for speech and voice recognition software will grow from 49 million in 2015 to 565.8 million in 2024.  

Promising Areas

        There are a myriad of potential applications for voice-activated technology. Consumer-facing markets have significant potential for growth, particularly mobile device authentication, control of wearable devices, and the Internet of Things. Healthcare, call centers along with government and enterprise IT are also seen as promising areas.

Mobile Devices and Applications
        Mobile devices have represented a breakthrough for voice-activated technology. By introducing built-in speech recognition capabilities, Google, Apple, and Microsoft’s mobile operating systems have shed light on the actual usefulness, convenience, and flexibility of such functionalities, which are increasingly central to the mobile experience. The numbers for Apple’s Siri make for a staggering example. Over 500 million people globally have access to the virtual assistant, more than 200 million use it monthly and 100 million use it on a daily basis.

        Many experts consider conversational interfaces the future of mobile applications. While conventional applications offer a limited number of interactions, which are restricted to the possibilities featured on a screen, voice interfaces are an open-ended model through which users can ask questions and issue voice commands that best suit their needs.

Biometrics
        Similar to a fingerprint or iris, voice is unique to each individual and can, therefore, be used as an authentication method. The voice biometric, a mathematical representation of the inimitable sound, pattern, and rhythm of an individual’s voice, is extremely difficult to forge. By measuring qualities such as dialect, speaking style to pitch, spectral magnitudes, and format frequencies, innovative voice authentication technology offers exceptional reliability, accurately pinpointing attempts to impersonate a voice or to provide voice recordings.

        The rise of identity theft rates as well as the increasing desire for rapid and secure access to information has steered companies towards voice biometrics. According to research firm Tractica, some vendors are building voice print databases for call centers, specially designed to identify repeat callers or known fraudsters.

        Banking is a particularly promising market for voice biometrics. For instance, Capital One recently partnered with Amazon to enable Amazon Echo users to pay bills and access account information through voice commands.  After suffering an online cyber attack, HSBC will implement voice recognition services in both mobile apps and telephone banking. The roll out is expected to reach 15 million customers by the summer. Barclays already resorts to voice authentication for 300,000 of its wealthiest clients in the UK and plans to expand it to 12 million retail-banking customers. The experience has been extremely successful, with the time taken to verify an identity falling from 1.5 minutes to less than 10 seconds.

Smart Cars and the IoT
        The Internet of Things is undoubtedly a major field for speech recognition.  The multiplication of connected, smart objects creates unprecedented need for interaction between man and machines. In this context, voice has gained ground as a privileged avenue of communication. Even though home automation is the most striking example of how voice commands can facilitate the control of IoT systems, smart cars are also a promising area for voice-driven innovation.

        Research firm TechNavio predicted a compound annual growth rate of 10.59 percent for the global automotive voice recognition market between 2013 and 2018.  An interesting example is Bellevue, Washington-based VoiceBox Technologies, which recently unveiled an embedded automatic speech recognition product for advanced automotive applications. Combining deep neural networks and natural language understanding technology, the solution is capable of processing complex, contextual conversations. VoiceBox’s flexible speech recognition system also enables enhanced safety since drivers do not have to ‘think about’ how to structure a command and are thus less distracted.


A Voice-Driven Smart Home Strategy

        Over recent years, Amazon has established itself as a major player in the speech recognition market. This is largely due to Amazon Echo, a smart speaker equipped with always-on microphones that are capable of receiving voice commands from anywhere in the room. Behind the wonders of cylinder-shaped Echo is Alexa, Amazon’s speech recognition system.

        Similar to other virtual assistants, Alexa gives hands-free access to a variety of functionalities, such as weather, calendar, and search. The difference is that, thanks to Echo’s seven embedded microphones and electrical connection, Alexa is always listening, no matter where you are in the room, no buttons pressed.

        Amazon Echo is experiencing unprecedented demand. In many instances, it has become the center of Amazon’s smart home strategy. Echo works as a control hub that connects various third-party home automation applications. Examples of compatible devices include smart lighting solutions, such as Philips Hue, Wink, and Samsung Smart Things, as well as Wemo’s and TP-Link’s smart outlets and switches. Compatibility with Honeywell and Nest thermostats should be introduced in the near future.

        Amazon recently unveiled two additions to its Alexa-based lineup: Amazon Tap, a portable version of Echo, and Echo Dot, a compact extension of Alexa that augments existing Echo installations. This range of speech recognition products undoubtedly points to the fact that Amazon’s voice-activated platform is at the heart of its strategy moving forward.


Conversation as a Platform

        An Internet or web bot is a software application that performs online, automated tasks. Microsoft is combining bots and speech recognition as the basis of its “conversation as a platform” strategy. The company recently unveiled a bot development framework at the Build 2016 developer conference, where CEO Satya Nadella also underlined the importance of speech recognition. In his words, "human language is the new user interface layer."

        Microsoft aims to offer developers the necessary code and machine-learning tools to build intelligent bots and link them to voice-driven digital assistants, like Cortana. Simplified developer experience and the possibility of establishing natural language communication with users are the focal points of the company’s innovative strategy. The vision behind “conversation as a platform” is to make bots that understand natural language the new way of using computers. In Nadella’s words, “bots are like new applications that you can converse with.”


Crossing the Offline Frontier

        Though offering significant speed and accuracy, Android’s speech recognition system, as many others, has one weakness: it is dependent on Internet connection. It works by recording the user’s voice and transmitting it to a server, where it is analyzed, converted into text and sent back to the device. With limited vocabulary, the offline system currently available is rudimentary, slow, and less powerful than the online version.

        Google is working to change this scenario. The search giant recently unveiled a research paper on an innovative system that is seven times faster than real-time and works without data connection. The company describes it as “a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-time on a Nexus 5 Android smartphone."

        The embedded system uses various machine-learning techniques and features an advanced acoustic model, based on approximately 3 million anonymous utterances extracted from voice search traffic (approximately 2,000 hours). In order to achieve enhanced accuracy, it was exposed to noise and reverberation using samples extracted from YouTube videos and environmental recordings.

        The application is remarkably small, weighting a little over 20 MB. Besides multiple compression techniques, the system incorporates both dictation and voice commands into a single module, an innovative configuration that enables reduced computational footprint.

        Google’s offline system has already been tested on a Nexus 5 and experts believe that improvements should be incorporated in the not so distant future. Crossing the offline frontier is, therefore, an important trend in voice-activated software innovation.


Start-Up Innovation

        The wealth of opportunities surrounding speech and voice recognition has triggered major action at the start-up scene. Palo Alto, California-based Wit.ai, for instance, helps developers build a speech interface for their applications or devices. Acquired by Facebook in January 2015, Wit.ai’s open platform is at the forefront of intelligent language processing. With over 10,000 users, its software is constantly learning and expanding human language capabilities.

        Founded by developers of Apple’s Siri, San Jose, California-based Viv Labs is the creator of Viv, a personal artificial intelligence assistant that can not only understand voice commands but also automate users’ requests. Intended to become a ubiquitous brainpower behind apps, devices, and machines, Viv’s remote artificial intelligence service aims to enable developers “to create an intelligent, conversational interface to anything.” The idea is to develop a type of artificial intelligence app store upon which third-party developers can build a personalized layer.

        Viv is designed to go further than other virtual assistants and overcome their limitations. For instance, while Siri is only capable of performing tasks that are explicitly programmed, Viv can teach itself, combining the users’ personal preferences and its web of connections to perform virtually any task.

        Also focused on powering a new generation of voice-driven applications, San Francisco, California-based MindMeld is yet another example of innovation in allying speech recognition and artificial intelligence technology. Founded in 2011, the company has developed a platform that provides infrastructure and customization options for users to create unique and intelligent voice interfaces for their apps and devices. Named by MIT one of the 50 smartest companies of 2014, MindMeld is currently working with major online services, including music streaming service Spotify.

        Start-ups working with voice-activated technologies can greatly benefit from R&D tax credits, particularly due to the burden of payroll costs, which are typically their largest expense. The R&D Credit has now been expanded to apply credits in excess of income taxes to FICA tax liability.  Notably, even if a company was unprofitable and had no tax liability, the credit can be taken against payroll taxes for start-up businesses with less than $5,000,000 in gross receipts.  This offset is capped at $250,000 per year, over a five-year period.

        As such, the R&D Tax Credit now allows this payroll tax to be taken directly against FICA taxes, and does not require general income tax liability for the company to utilize the credit amounts. The company will realize significant tax benefits regardless of not generating a profit.  Most importantly, this credit directly affects the payroll amounts incurred by the start-up.

University Research

        Numerous U.S. academic institutions are also fueling natural language processing innovation. The following examples shed light on current trends in voice-activated technology R&D.

Carnegie Mellon University
        As the result of over 20 years of work, researchers at Carnegie Mellon University have developed CMUSphinx, an open source toolkit for speech recognition. Designed specifically for low-resource platforms, it uses state-of-the-art algorithms that allow for highly efficient speech recognition. In addition to speech decoders, Sphinx encompasses software for acoustic model training, language model compilation, and a public domain pronunciation dictionary. Sphinx works as the basis for a wealth of speech recognition research. Examples include Open Ears, which supports not only English, but also Spanish, Mandarin, French, German, and Dutch, as well as ILA, a fully customizable, teachable voice assistant.

University of Texas at Dallas
        Researchers from the Erik Jonsson School of Engineering and Computer Science at UT Dallas are currently developing speech recognition tools capable of understanding human emotion. The idea is to create innovative algorithms that identify spontaneous behaviors in speeches and interpret the underlying process of externalization of emotions. Associate professor of electrical engineering Dr. Carlos Busso anticipates the use of emotional-aware technology in several areas, including security and defense, next-generation advanced user interface, health behavior informatics and education.

Johns Hopkins University
Established in 1992, the Johns Hopkins Center for Language and Speech Processing (CLSP) is an interdisciplinary initiative aimed at advancing knowledge and technologies for language processing. Examples of ongoing research include automatic speech recognition in reverberant environments, which focuses on creating accurate systems capable of working in noisy environments with varying acoustic conditions and microphone configurations.

Massachusetts Institute of Technology
Mobile applications are a highly promising field for speech recognition technology. An interesting example is the work of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who have applied advanced language-processing technology to develop a nutrition-logging system. Though a proven way to lose weight, logging nutritional information at every meal is a time-consuming and tedious task. Aimed at helping people that struggle with obesity, the innovative speech-controlled application allows users to verbally describe the contents of a meal. It then reviews the description and automatically retrieves pertinent nutritional data from the U.S. Department of Agriculture database.


Conclusion

        Voice is bound to become the premier means of communication between man and machine. Recent advancements in voice and speech recognition systems are only a glimpse into the wealth of opportunities that lie ahead. Director of the Johns Hopkins Center for Language and Speech Processing Hynek Hermansky addresses this promising future by saying: “Just as the early successes of the Wright brothers in flight by machines heavier than air immediately spurred a frenzy of research in relevant aeronautic technologies that gave rise to the current air travel industry, the early successful deployments of language technologies only suggest enormous possibilities of truly human-like language interactions with machines.” R&D tax credits can have a strategic role in supporting innovative companies succeed in the emerging, voice-driven world.

Article Citation List

   


Authors

Charles R Goulding Attorney/CPA, is the President of R&D Tax Savers.

Jennifer Reardon is a Project Coordinator with R&D Tax Savers.

Andressa Bonafé is a Tax Analyst with R&D Tax Savers.


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