Where's Jarvis? The Future of Voice Recognition and Natural Language User Interfaces UXPA 2016
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Where's Jarvis? The Future of Voice Recognition and Natural Language User Interfaces UXPA 2016
- 1. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Where’s Jarvis? The Future of Voice Recognition and Natural Language User Interfaces. Crispin Reedy, Versay Solutions @crispinTX crispinreedy.com #UXPA2016
- 2. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions From the session description • What is voice recognition? • What is natural language understanding? • What are the common technologies in the market today? • How does this fit with IoT? • What are design considerations / methods to evaluate these types of interfaces? • Implied: Should I speech-enable my ___? • Bonus Q: Why doesn’t it work the way we want it to, and when will it?
- 3. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Should I Speech-Enable My ___?
- 4. Iron Man 2: Marvel Studios, Paramount Pictures
- 5. Star Trek Voyager: Paramount Television
- 6. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions “Tomato soup” “Tomato soup. Ok, what kind?” “Just plain” “Coming right up!” Implicit confirmation Second level-open ended prompting Cultural context: plain = hot
- 7. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Terms & Technologies • Speech Recognition • Natural Language Understanding • Voice Verification (Biometrics) • Text to Speech
- 8. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Speech Recognition “ASR” “See the cat.”
- 9. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Natural Language Understanding • Extracting meaning from natural text “Hello, yes, I’d like to pay my water bill. Can you help me with that? Intent = BillPay Entity (Bill Type) = Water
- 10. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Voice Verification “My voice is my password.” “Authenticated. Welcome, Mr. Smith.” ✓
- 11. Text To Speech
- 12. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions What Is Good TTS? • Phonemes change based on location • “Cat” • “Alligator” • Elision • “I’m. Awaiting. You.” • “I’m awaiting you.” • Intonation • “Do you want coffee?” • “Do you want soda, tea, or coffee?” • Most TTS isn’t “Movie Quality” IMDB
- 13. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions SSML Example SSML
- 14. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Speech Recognition • Hands-free command / control • Dictation • Input text • Small form factor device, etc. Text To Speech • Output text dynamically • Respond to input • Useful when no display is available Natural Language Understanding • Necessary for all language-based input • Extract meaning • Parse large volumes of text Voice Verification • Security
- 15. ASR Application Data • Sign-In • Interaction • Request • Action • Meaning • Access Data • Output TTS NLU Voice prints Verifi- cation
- 16. ASR Application Data • Sign-In • Interaction • Request • Action • Meaning • Access Data • Output TTS NLU Voice prints Verifi- cation Touch Keyboard Manage I/O Modality Determine Meaning in Context Visual Context!
- 17. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions
- 18. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions ASR
- 21. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Speech is ambiguous
- 22. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Language is ambiguous
- 23. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Everything is ambiguous
- 24. Speaker Independence Speaker Dependent Multiple Speakers Speaker Independent Isolated Words Connected Words Natural Speech 10 words 1000 words 100,000 words Unlimited VocabularySize Humanlike
- 25. AUDREY: Automatic Digit Recognizer Bell Labs 1952
- 26. X — states y — possible observations a — state transition probabilities b — output probabilities "HiddenMarkovModel" by Tdunningvectorization: Wikimedia
- 27. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Training Speech Recognition Engine Acoustic Model SLM and/or Grammar Pronunciation Model
- 28. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Utterance Noise Levels? Barge-In? Feature Extraction Endpointing Speech Recognition Engine Grammar or SLM Probabilities n:best list Literal return Tokens Recognition Event
- 29. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Early Commercial Adoptions • Interactive Voice Response • “Those Phone Menus” • Server-based ASR • Nuance • Microsoft • Voice-Enabled Handheld Devices • Industrial / Productivity applications • Device-based ASR • Network not needed Note: Call center is still an important customer touchpoint!
- 30. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Today’s Speech Agents vs. APIs • Siri / Apple APIs • Cortana / Cortana APIs • Google Now / Google Voice Actions • Amazon Echo (Alexa) / AVS API • Jibo • Ubi / Ubi Kit • Assistant.ai / Api.ai
- 31. Alexa Skill vs. Amazon Voice Service Amazon.com
- 32. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Alexa Skill Example Amazon.com
- 33. Amazon.com
- 34. Capitol One.com
- 35. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions NLU
- 36. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Natural Language Understanding • Parsing input to extract meaning • Covers a large field • Commands • Automatic classification of emails • Newspaper articles, large chunks of text • Bots • Conversational agents • Messaging apps • Personal assistants • Input could be via speech or via text
- 37. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Levels of Meaning Too Broad / Ambiguous Too MuchJust Right “I’m having a problem with my account.” “Well, I was looking at my bill, because I do that every week, and I was reviewing everything on there, and I saw…” “I’m seeing an unusual charge on my bill.” “How can I help you?”
- 39. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Intents and Entities • “I’d like to transfer $50 from my checking account to my savings account.” • ACTION = Transfer (Intent) • FROM_ACCOUNT = Checking (Entity) • TO_ACCOUNT = Savings (Entity) • AMOUNT = $50 (Entity)
- 40. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions NLU APIs • API.ai • Alexa • Microsoft LUIS • Wit.ai • Google Voice Actions • Etc.
- 41. Today’s NLU APIs • Microsoft LUIS (part of Project Oxford) Microsoft.com
- 42. Today’s NLU APIs API.ai| • API.ai
- 43. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions The Future Is Here • DNN (Deep Neural Networks) • Being applied to both ASR and NLU problems • Requires large amounts of data to train the models
- 44. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions What’s The Glue Here? Consistency Across Contexts? “Omnichannel CX” Data Is Everywhere State Chart XML?
- 45. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions ASR vs. NLU: Wrap Up ASR • Spoken aloud • Requires some NLU even if it’s hand-crafted (tagging) • Useful in hands-free, eyes-free contexts NLU • Focuses on meaning extraction • Could be used for chat bots, etc. • Machine learning to train models
- 46. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Design Considerations
- 47. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Design Considerations • What are you trying to build? • What’s your platform? • Existing guidelines / research • User testing is key • Especially if you’re trying to do something complicated
- 48. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Should I Speech-Enable My ___?
- 49. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions What’s Your ASR/NLU Platform? Write an app (skill) for an agent such as Cortana / Alexa Use cloud APIs to add ASR / NLU to your app / device / page / gadget Download software and use full-featured capabilities for more robust recognition on a specific device Build your own
- 50. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Network Availability • Simply irritating… or totally unusable? “What’s on my calendar today? “Sorry, I can’t complete that request right now.”
- 51. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Appropriate Modality? • Voice Only? Voice + Display? • Is it possible for the user to switch modalities? • Or would switching potentially be dangerous? “How long is the flight from Dallas to Seattle? “I’ve got a few results to show you.”
- 52. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Is State Maintained? • Does your platform support a multiple-stage interaction? • Does it remember what you did previously? “Who is Barack Obama?” “Barack Obama is the 44th president of the United States.” “How old is he?” “I’m sorry, I don’t understand your question.”
- 53. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Wake-Up Words • How many of these “Agents” will we be talking to? “Jibo, take a picture.” “Alexa, play music.” “OK Google, set the temperature to 77 degrees.”
- 54. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions System Personality • Are you writing for an “Agent” who has an existing style? • What if your skill or app doesn’t match that style? • If not, should you create one? “Hi, I’m Julie!”
- 55. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Context • Real-world context • Digital context • How much does your app know about where you are and what it can do? “When I get home, remind me to take out the trash.” “I’m sorry, your calendar doesn’t support location- based reminders.”
- 56. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions What Are You Trying To Recognize? • Long utterances work better than short ones • Letter names require extra work “Start a session” “Got it”
- 57. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions And So Much More…. • What will you do when the recognizer just can’t get it? “I want my…. BARK BARK BARK Timmy STOP THAT NOW GET DOWN!” ????
- 58. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Existing Guidelines / Research • Caveat: Best practices evolved in one modality (e.g. voice-only) may not apply the same way in another (e.g. combined voice + touch) • But they could be adapted • Association for Voice Interaction Design (AVIxD.org) • Wiki • Peer-Reviewed Journal • Virtual “Brown Bags” • Academic Sources, Books
- 59. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions AVIxD.org CUI Working Group is actively recruiting!
- 60. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Specific Example: “Help” Voice XML Standard (2004) “Help” should be a global command AVIxD Wiki (2014) Stop using “Help” as a global Agent API Doc (2015) Offer “Help”
- 61. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Specific Example: “Help” • Designers who tune applications have seen that the word “help” is a known “False Attractor” • Other things that you say which are short get recognized as “help” • People don’t voluntarily come up with “help” unless they are prompted • Give callers a context specific command only where help may truly be needed, and call it something besides "help” • System: Say or enter your account number, or say, where do I find it.
- 62. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Special Case: Car • “Distracted Driver” is a hot topic! • Richard Young, Wayne State University • Paper: “Safe Interaction For Drivers” • “Visual-Manual Mode” – What we do today • “Auditory-Vocal Mode” – Speech only. NO GUI. • “Mixed Mode” – Speech and GUI being used together • Finding: If you give someone a graphic interface, they’re going to look at it • And take their eyes off the road
- 63. Design Documents
- 64. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Usability Studies / Research • Special Challenges • Technical setup • Phone tap / Recording both sides
- 65. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Warner Bros.
- 66. Early Stage Voice Only Prototype
- 67. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Should I Speech-Enable My ___?
- 68. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions What’s the Use Case? • Enabling application • User can’t do it any other way • New tasks • Enhancing application • User can do it now • But speech makes it better • Faster • Safer
- 69. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions API-Based Device- Based Roll Your Own / Open- Source • Flexibility • Power • Customization • Time • Difficulty
- 70. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Cloud vs. Downloadable / Embedded • Easy to get started • Lightweight • Not much specialized knowledge • Customizable • Probably better recognition • Can be device-specific • More features • Higher powered • May require specialized knowledge – Speech scientist
- 71. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Open Source ASR • CMU Sphinx • pocketsphinx • Kaldi • http://kaldi-asr.org/ • Github • New updates include some pretty interesting stuff (DNN) • Requires: • Corpus • Tech know-how
- 72. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Should I Speech-Enable My ___?
- 73. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Should I Speech-Enable My ___? Maybe
- 74. Iron Man 2: Marvel Studios, Paramount Pictures Where’s Jarvis?
- 75. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Where’s Jarvis? Gesture Based Interface Artificial Intelligence Voice Based Interface
- 76. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Where’s Jarvis? ASR NLU Voice Design Context
- 77. #UXPA2016Session Survey: http://www.uxpa2016.org/sessionsurvey?sessionid=321© 2016 Versay Solutions Resources • Handout / Web page
Editor's Notes
- Voice User Interface Designer 10 years in the field English major, former coder; got interested in UX President of the Association for Voice Interaction Design Consultant for Versay Solutions 2 weeks in a row for conferences
- Jarvis: Audio and gestural Perfect recognition. No error recovery needed Great voice quality Connected to vast amounts of data Understands all the parts of the model: “Lose the landscape.” Context-sensitive. Aware of the space around him Sense of humor. “Am I to include the Belgian Waffle stands?” Takes initiative. “What is it you’re trying to achieve, sir?”
- Replicator: Good recognition No error recovery needed Good voice quality – understandable Connected to data – perhaps too much so? Context sensitive- but was this enough? A design failure (not a tech failure) Specifically around excessive disambiguation
- A Better Replicator Conversation
- “Speech to Text” ? Spoken Language – Machine readable format
- Not necessarily tied to speech recognition
- Also called voiceprints, biometrics, voice authentication, etc. Not going to discuss this one in a lot of detail today but it’s important that you understand the difference between these technologies. Recognizes a person, not necessarily what they are saying. You can have ASR without Voice Verification And vice versa
- Human voice talent Hundreds of hours of recording Digitized Phonemes: Concatenated speech synthesis
- Dynamic Speech Synthesis Many commercial products are available API-based Downloadable Quality varies If possible, record audio TTS has improved considerably, but is still noticeable High quality TTS may not be available in all situations If you have a lot of dynamic data TTS is useful You can mix recorded audio and TTS You may have to use TTS Voice Agent (Alexa, Cortana, etc.) API-based Some of them do let you mark up your TTS with SSML More phonemes = higher quality voice Also means a bigger download and install (if on device) Exceptions (addresses, names) can be iffy May require a lot of work to handle well St. James St. Saint James Street Punctuation Your data needs to be clean and ready to voice back Acronyms, incomplete sentences will not sound good It is possible to build a custom voice But it takes a lot of work!
- Speech Synthesis Markup Language XML based WC3 standard Not universally supported Tags which allow you produce a more natural quality output. Emphasis Break Voice Prosody Pitch
- World Knowledge: Concepts of the world around us, i.e. Tables have four legs, what is left and right, what is a car, etc. This is the level before language Semantics: The first level of language. Knowledge can be represented in structured meaningful elements. Example: semantics of a party invitation Syntax: The rules that govern putting words together to form meaningful units Lexicon: What words mean Morphology: How words change their form to perform differently in a language i.e. horse / horses Phonetics: Phonemes and how words are built Acoustics: What phonemes sound like and how to create them
- Speech is never stationary Coarticulation Noisy environments Accents Different speakers have voices with different acoustic qualities Goats Challenges vary depending on what you are going to recognize Spelling (short utterances) can be difficult even for humans Phonetic alphabet (Military)
- Humans can deduce meaning from context and unknown words “How can I help you?” I’m having a problem with my account. I’d like that one. No, not the green one, the red one. Time flies like an arrow. Fruit flies like a banana.
- All modern speech recognition is probabilistic GUI: Button clicked? true / false VUI: There is an 85% chance that button was clicked
- Three Dimensions of Speech Problems
- AUDREY: Davis, Biddulph, and Balashek - Bell Labs 1952 Analog Isolated digit recognition Pause between digits Speaker-dependent Speech recognition with vacuum tubes – How very steampunk. Her name was AUDREY. Let that sink in a minute. (Automatic Digit Recognizer)
- 1980’s: The Power of Statistics The recognition of connected speech becomes a search for the best path in a large network Problem of finding the probabilities Statistical Language Models Not all sequences of words are equally probable Rank all permissible sentences in terms of probability “Correct” grammar is not applicable Restricted by domain Hidden Markov Models (HMM) Unified probabilistic model for speech
- You’re Only As Good As What You’re Trained On Corpora Collection of speech used to train a recognizer Acoustic and/or Pronunciation Model Associates sounds with symbols and words. Created by a general speech corpora and a phonetic and orthographic transcription Statistical Language Model (SLM) A probability distribution over sequences of words Created by a domain-specific speech corpora and a tagged transcription to extract meaning
- Speech Agent: The “Person” who Distributed speech recognition Collection and compression of speech is on the device The language models are typically on the network Phone can be speaker-dependent Trains itself on your voice and on the acoustic environments you are in most often Many companies are providing APIs to use their speech recognition
- Alexa, Ask Capitol One What’s my current credit card balance?
- Observations to make: Represents the entirety of a VUI experience Placement of Spanish prompt would vary depending on type of call. Confirmation is variable Confirmation prompt is general
- What do you need it for? What kind of device will you be running it on? Connectivity? Can you use cloud based ASR? How much control do you need over the application / user interface?
- Jarvis: Audio and gestural Perfect recognition. No error recovery needed Great voice quality Connected to vast amounts of data Understands all the parts of the model: “Lose the landscape.” Context-sensitive. Aware of the space around him Sense of humor. “Am I to include the Belgian Waffle stands?” Takes initiative. “What is it you’re trying to achieve, sir?”