THE RELEVANCE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN SPEECH RECOGNITION. A White Paper by Uniphore Software Systems

Transcription

1 THE RELEVANCE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN SPEECH RECOGNITION A White Paper by Uniphore Software Systems

2 Executive Summary Communicating with machines something that was near unthinkable in the past is today the driving force of new generation Speech Recognition solutions. The use of technically smart devices and the increasing human interaction with machines in fields like speech technologies is testimony to how Speech Recognition-based solutions are driving business dynamics. Speech Recognition and Speech Analytics allow enterprises to identify and address consumer needs, enabling these enterprises to offer better customer support and identify new business opportunities during interactions with their customers. The use of path-breaking technologies like Artificial Intelligence (AI) and Machine Learning (ML) in Speech Recognition solutions is today helping enterprises deliver smarter services. Businesses are able to increase their digital relevance quotient by being proactive rather than reactive and are reaching newer audiences as well. The aim of this Whitepaper is to throw some light on how modern Speech Recognition tools have forayed into adoption of technologies like AI and ML to usher in a silent revolution in the Speech Recognition technology. 1

3 Introduction to Speech Recognition (SR) Technology Throughout the evolution of human history, speech has been one of the fundamental modes of communication. Merging the ability of speech to relay information with the use of advanced tracking tools acts as a fundamental pillar of modern day Speech Recognition. Essentially, Speech Recognition (SR) is a combination of Linguistics, Computer Science, Electrical Engineering, and Statistics, allowing for recognition and translation of spoken language into text using smart technologies and devices. A Speech Recognition solution recognizes the words and phrases spoken and converts them into a machine readable format, paving the way for a human-to-machine communication. The spoken audio when converted into machine readable text allows the user to control the machine or the digital device just by speaking, replacing the use of traditional input methods like using keystrokes, button clicks, or screen taps. Speech Recognition technology can be better understood correlating it with how our human body recognizes speech. Science has proven than humans detect speech using our ears. People identify the meaning of the words using the left side of their brain, which is more analytical, and decode the associated emotions and expressions using the right side of their brain, which is more holistic and creative. Speech Recognition uses a similar task break up to reproduce a similar set of functions to analyze sounds and speech. Prevalent speech recognition solutions make use of machine-based recognition, allowing them to recognize speech based on pre registered words and sentences. 2

4 Overview of various application of Speech Recognition technology Speech Recognition solutions allow consumers of various brands to interact with the brand, replacing in part the need for the traditional customer service agent. Speech Recognition is eventually driving the DIY Customer experience, helping enterprises build smarter brands. For example, ridesharing service Uber 1 uses Speech Recognition solution allowing for a hands-free experience when booking a cab. Speech Recognition involves the use of a voice-based command system in in-car systems. From initialing phones to changing music playlists, Speech Recognition and in-car systems are slowly replacing manual control input. SR technology enables the use of voice biometrics as a fool proof authentication system to authorize access. In an era of rising digital crimes, voice biometrics based on Voice Recognition is a game-changing technology to prevent fraud. Military forces are using Speech Recognition technology in their high performance aircrafts and air traffic control. People with disabilities are being helped by Speech Recognition-driven tools to input commands using voice replacing text. SR technology is also playing a dominant role in restoring short term memory loss for people suffering from stroke, leading to a whole new world of possibilities in the healthcare sector. Introduction in brief - AI (Artificial Intelligence) and ML (Machine Learning) In a world besieged by the relentless advance of digital technology, terms like Artificial intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have become quite common. Often, these terms are used interchangeably, though there is a clear demarcation between them. The one common denominator that binds all such terms like Ml and AI is that they help evolve a machine-intelligence environment, simplifying human-machine communication. While AI and ML have their own dedicated spheres of use, AI is best understood as a branch of computer science that allows for building smart machines capable of behaving intelligently in the right environment. ML, on the other hand, is the science of getting these machines or computers to act smartly without being programmed excessively. 1 Source: 3

5 Eventually, AI experts and researchers build smart machines, but ML experts are needed to make such machines truly intelligent. Artificial Intelligence (AI): ArtificiaI Intelligence is all about making machines intelligent using advanced computer intelligence. The core driver of AI based technology is to be able to create a mach i n e o r a co mp u ter th at can act j u st as intelligently as a human mind does. At its core, AI is based on various disciplines like Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. For example, if a computer program is created without AI capability, it will answer only to the specific question or problem it is meant to solve. On the other hand, if a program is developed using AI, it will not only answer the specific question but also answer related general questions but understanding the questions intelligently. AI-based Speech Recognition tools understand not only languages spoken by their users, but also can track emotions, accents, and behavior patterns using speech modulation driven by AI. Machine Learning (ML): Machine Learning can be best understood as a subset of AI whereby the smart AI capable machine uses large data sets to learn on its own. ML-based systems make use of these large data sets, apply training algorithms, and develop knowledge from those data sets. ML eventually allows programs to recognize patterns and make appropriate predictions based on the same. Many ML-based Speech Recognition systems, for example, offer sales analysis by gauging and correlating a customer s mood with his or her likelihood of being receptive to a sales offer. 4

6 Configuration of business rules: AI and ML allow Speech Recognition applications to customize as per their core business rules. AI, with its advanced keyword recognition system, aids Speech Recognition programs in monitoring agent compliance and associated KPIs. For example, using Speech Recognition in an industry where a disclaimer is essential as per regulation, AI-based keyword tracking can ensure the agent delivers the disclaimer beforehand while tracking consumer s response. Application of AI and ML in various Speech Recognition-based functionalities Some of the smart Speech Analytics software make use of AI and ML capabilities, allowing contact centers to drive critical business goals. This is done as the applications are able to analyze existing speech data to build statistically strong models and enrich it with live data to predict outcomes with high confidence levels. The use of AI- and ML-based solutions allows Speech Recognition applications to learn about changes in user behavior smartly, which in turn helps them predict future behavior or engagement pattern. Self-learning dialect adaption: Speech Recognition applications may track a user s language but changing over to dialect tracking by adopting a self learning mechanism is possible only with ML. This has immense applications in an increasingly globalized and interconnected world. Emotion detection and tracking: AI and ML allow Speech Recognition tools to track consumer emotions using voice modulation and pitch analysis. Such a tracking can be invaluable for fine-tuning engagement strategies, prioritization of consumer needs, or timing a sales pitch. 5

7 Offering descriptive and diagnostic analysis: Adopting AI and ML allows a Speech Recognition program to become a truly predictive one allowing for a thorough descriptive and diagnostic analysis. Tracking KPIs and identifying drivers for such KPIs are possible only when ML is a core module of the Speech Recognition application. How use of AI and ML in Speech Recognition is helping scale it The significance of AI and ML in Speech Recognition technology can be gauged from the fact that all SR-related research work is moving towards increasing accuracy. Since AI and ML are technologies that make a Speech Recognition application more customizable, accurate, and intelligent, they are parts of all major Speech Recognition research. 6

8 The use of AI and Ml tools have today ensured that Speech Recognition is now spreading its wings across industry verticals and is not limited to a handful of sectors. For example, Microsoft s Artificial Intelligence and Research Unit has reported 2 that its Speech Recognition technology has surpassed the performance of human transcriptionists, making it one of the most accurate systems ever. Microsoft first introduced its Speech Recognition technology alongside its popular OS Windows 95. With Cortana, Microsoft s latest phone assistant now built into Windows 10 that uses AI and Ml based Speech Recognition technology, it offers almost 90 percent accuracy. Web search giant Google has a similar Speech Recognition story to tell. Its AI experts have predicted that, by 2019, half of web searches will be through speech and images. Working overtime to improve its Speech Recognition technology, Google currently offers voice search with an accuracy rate of 92%. Its Speech Recognition technology is offered to consumers via the Google app for voice diction on Android phones. 2 Source 1: Source 2: 7

9 Speech Analytics helping enterprises drive business goals Speech Analytics is today one of the most significant tools used by enterprises to derive critical business goals. While Speech Analytics improves the efficiency of contact center agents, its ability to surface hidden trends and patterns is pure gold for business plans and growth. In today s era with ever changing consumer needs and habits, only those enterprises that track the communication footprint of their clients can hope to stay ahead of their rivals by devising newer products and services. Speech Analytics, with its dual advantages of addressing consumer needs and preferences and decoding new business opportunities, is therefore key when it comes to extracting insights from customer communication. Speech Analytics has come a long way from offering pre-defined analytics to becoming proactive and smarter using AI- and ML-based methodologies. Thus, smarter Speech Analytics programs demonstrate higher accuracy rates, helping business track essential micro trends with 100% tracking of all digital communication. 8

10 aumina from Uniphore: AI and ML capabilities Uniphore s Speech Analytics solution aumina is driven by AI and ML abilities, allowing clients to configure business outcomes and measure success rates, as well as integrate external data within the system. With its AI and ML capabilities, aumina combines multiple smart approaches and end user benefits like refined audio quality, latent business insights, and visual analytical engines to drive its Speech Analytics offerings. How enterprises gain by refined quality of conversations in SR: Smart Speech Recognition tools are today offering enterprises insights and analytics from just by analyzing voice conversations. aumina offers an inbuilt refined audio quality tool helping enterprises seek an error free analysis. As a result enterprises are able to increase accuracy and improve output. aumina with its patented algorithms enhances the quality of conversations offering a much deeper and refined analysis. The ML capabilities help aumina analyze voice conversations while dynamic processing helps in selection of the best speech engine without any user intervention. AI-ML capabilities of aumina: A business analyst s delight: Speech Recognition tools are helping business analysts convert any unstructured data into a structured form for interpretation and analysis. The use of AI and ML in aumina, for example, helps analysts configure business outcomes proactively. With AI capabilities, Business Assistants can now learn from multiple configurations, leading to insightful interpretation. Just by adopting smart Speech Recognition tools, analysts can achieve the length and breadth of business insights earlier considered too difficult to track. 9

11 How aumina helps enterprises identify root causes of problems smartly: Interactive data analysis offered by Speech Recognition programs had largely been text-oriented in the past. The use of AI and ML capabilities of aumina is now allowing businesses to seek visual resources for interactive data analysis. The coming together of visualization and analytics allows the enterprise to drill down and identify root causes of any tracked issues with ease. For example, aumina s visually rich dashboard allows the user to configure and tune the visuals as per the needs of the enterprise, leading to faster identification of RCAs. Conclusion AI and ML in Speech Recognition solutions are helping enterprises deliver smarter services and achieve business outcomes that were until now unviable. While Speech Recognition-based solutions have been driving business dynamics for a while, the added functionalities of AI and ML are aiding analysts in tracking and decoding contact center interactions, giving enterprises newer perspectives with each such insight. To know more about how your organization can benefit by implementing AI- and ML-based Speech Analytics using aumina or deploy a smart Speech Analytics program customized for your needs through a demo, please write in at: bd@uniphore.com 10

12 Uniphore Software Systems is a frontrunner in the Speech Recognition Technology and Virtual Assistant domains. It partners with over 70 enterprise clients and has over 4 million end users. Uniphore was recognized by Deloitte as a Technology Fast 500 company in Asia Pacific in 2014 and was also ranked as the 10th fastest growing technology company in India by Deloitte Fast 50 in Umesh Sachdev, Uniphore s Co-Founder & CEO, figured in the TIME Magazine s 2016 list of 10 Millennials Changing The World, and in India s edition of MIT Technology Review s Innovators Under 35 for the year Uniphore was incubated in IIT Chennai, India in The company is headquartered in IIT Madras Research Park, Chennai. It has offices in India and Singapore, with about 100 employees spread across both locations. Uniphore s investors include Kris Gopalakrishnan, IDG Ventures India, India Angel Network, Yournest Fund, and Stata Ventures.