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เสียงพูด, อินเทอร์เฟซผู้ใช้ตามบทสนทนา 2019-2029: เทคโนโลยี, ผู้เล่น, ตลาด
ระบบเสียงอัจฉริยะ, ลำโพงอัจฉริยะ, ระบบช่วยเสียง, ส่วนติดต่อผู้ใช้ที่เปิดใช้งานเสียง
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Natural human-machine interface is shaping our life
From punch cards, to keyboards, from mouse to touch screens, technologies have shaped the way how humans interact with machines. "Human-machine interface" (HMI) began as "computer interface". That is because early computers were not interactive and gradually "human-machine interface" became "human-machine interaction". "Interaction" is the first revolution that occurred in the development of human-machine interface. Now we are experiencing the transition to "natural user interface", which is considered to be the second revolution of HMI.
Machines/computers can interpret natural human communication and they communicate more like humans.
Compared with keyboards and mouses, touch is considered as a natural interaction. Apart from touch, audio and vision modalities can also provide new ways of interaction.
Figure1 Evolution of human-machine interactions
Source: IDTechEx
Speech/Voice-based interaction
Speech enables a convenient integration. It is hands-free, eyes-free and keyboard-free. As talking is natural for most of us and it does not require us to learn new skills, the learning curve is low. Humans can speak 150 words on average per minute compared with 40 when typing. Speech interaction can be quickly mastered by young generations, old people, disabled people and illiterate people. It can also be applied in occasions and devices where common interactions are challenging such as while driving, without light, or in extremely small wearables. These advantages make speech an increasingly popular media for devices and applications.
Speech recognition (SR) is the "ear" of a machine, which is the basis for speech user interface as the input enables the whole interaction process. Speech recognition was first introduced in the 1920s when a toy dog "Radio Rex" could come when his name was called. Speech user interface was also applied in vehicles in early times. However, the poor recognition accuracy and bad user experiences stopped it going further. Since 1993, the accuracy of speech recognition had been stagnated around 70% based on traditional model, which led to the poor user experiences as users could easily get frustrated and lose patience during the process. It was machine learning, or more specifically, deep learning, that significantly increased the accuracy of speech recognition in 2010s when they have been proved to be effective in improving the recognition accuracy. In 2016, Microsoft reported a speech recognition system reached human parity with a word error rate of 5.9% and in 2017, Google reported an accuracy of 95%. The technology improvement indicates that machines can be as good as human beings in terms of "hearing" and now speech recognition has become a commodity.
Giants such as Apple, Amazon, IBM, Google and Microsoft, all have efforts on smart speech. Besides the "ear", it is also vital for the machines to have the "brain", "mouth" and other organs to realize natural language speech interactions. In this process, emerging technologies and business models are established.
Speech is language-dependent, making the global market more complicated and segmented. However, the general focus of global market is English-centric interactions, with a few popular language systems developed by local players due to their strengths in language data.
Figure 2 Spoken dialogue system processes
Source: IDTechEx
This report provides an introduction of different technologies from both hardware and software point of view from the scratch. They are listed as following:
- Voice-enabled smart speakers
- Microphone arrays
- MEMS speakers
- Voice system on chip
- Machine learning
- Front-end signal processing
- Key word spotting
- Automatic speech recognition
- Natural language understanding
- Speech synthesis
- Voice recognition
- Machine translation
Figure 3 Value chain
Source: IDTechEx
Market landscape, business models and value chains are analysed in this report, with a ten-year market forecast on the angle of revenue model and applications for the following sectors:
- Automotive
- Banking, financial and insurance
- Healthcare
- Travel, hotels
- Retail/commerce
- Home automation
- Education
- Game & entertainment
- Voice-enabled smart speakers
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| 1. | EXECUTIVE SUMMARY |
| 1.1. | Transition of human-machine interface |
| 1.2. | Is the times of natural language interaction coming? |
| 1.3. | Why natural language UI is disruptive? |
| 1.4. | Driving force |
| 1.5. | Influence of speech UI |
| 1.6. | Market demand of speech technologies |
| 1.7. | Entry barriers |
| 1.8. | SWOT analysis of speech UI industry: strengths |
| 1.9. | SWOT analysis of speech UI industry: weaknesses |
| 1.10. | SWOT analysis of speech UI industry: opportunities |
| 1.11. | SWOT analysis of speech UI industry: threats |
| 1.12. | Profit level |
| 1.13. | Product life |
| 1.14. | The cards in giants' hands—Google, Microsoft, Amazon, Facebook, Apple, IBM |
| 1.15. | Giants' activities |
| 1.16. | Popular development models in speech-related business |
| 1.17. | Technology trend |
| 1.18. | Hype or hope |
| 1.19. | Value chain |
| 1.20. | Changes in the value chain |
| 1.21. | Open-loop system or not |
| 1.22. | Revenue models of speech products |
| 1.23. | Market forecasts - assumptions & methodology |
| 1.24. | Market forecasts 2018-2029 by revenue channel |
| 1.25. | 2019 & 2029 market values by revenue channel |
| 1.26. | Analysis of market forecast 2019-2029 by revenue channel |
| 1.27. | Market forecasts 2018-2029 by application |
| 1.28. | 2019 & 2029 market values by application |
| 1.29. | Analysis of market forecast 2018-2029 by application |
| 2. | INTRODUCTION |
| 2.1. | Evolution of human-machine interactions |
| 2.2. | Natural user interface |
| 2.3. | Questions about natural user interface |
| 2.4. | Overview of speech UI |
| 2.5. | Voice interaction products at a glance |
| 2.6. | User interface and application programming interface |
| 2.7. | Speech: alternative to keyboard |
| 2.8. | Evolution of speech user interface |
| 2.9. | Benefited from high speech recognition accuracy |
| 2.10. | Timeline of speech recognition error rate |
| 2.11. | Human parity has been achieved |
| 2.12. | Voice search is taking an increasing share |
| 2.13. | Reasons for using voice |
| 3. | SMART SPEAKERS |
| 3.1. | Timeline of smart speaker release |
| 3.2. | Voice-activated smart speaker product list |
| 3.3. | Amazon Echo |
| 3.4. | Amazon Echo Dot |
| 3.5. | Alexa devices |
| 3.6. | From Google Now to Google Home |
| 3.7. | Google Home teardown |
| 3.8. | Comparison of Amazon Echo and Google Home |
| 3.9. | Apple HomePod |
| 3.10. | Little Fish powered by Baidu |
| 3.11. | Levono |
| 3.12. | Smart speaker comes as voice activated home hubs |
| 3.13. | The success of Amazon Echo |
| 3.14. | Amazon Alexa |
| 3.15. | Integration and centralization |
| 3.16. | Amazon Web Services |
| 3.17. | The numbers behind Amazon Echo |
| 3.18. | Surveys around Amazon Echo |
| 3.19. | Things work with Amazon Alexa: smart home |
| 3.20. | Things work with Amazon Alexa: other devices and service |
| 3.21. | What do developers and users want Amazon Alexa for |
| 3.22. | Competition strategies |
| 3.23. | Move away from hardware sales |
| 3.24. | Interoperability between Amazon, Apple & Google ecosystems |
| 3.25. | Smart speaker market status |
| 3.26. | Estimated sales of major voice-activated smart speakers |
| 3.27. | Smart speaker market forecast |
| 4. | TECHNOLOGY |
| 4.1. | Speech technologies |
| 4.2. | Smart speaker core components |
| 4.3. | Smart speaker hardware: speaker design |
| 4.4. | Smart speaker hardware: circuit board, communication and battery |
| 4.5. | Microphone Arrays |
| 4.6. | Amazon Echo's 6+1 microphone array |
| 4.7. | AISpeech's microphone array solutions |
| 4.8. | Ding Dong R7+1 microphone array |
| 4.9. | Microphone array trends |
| 4.10. | MEMS microphones |
| 4.11. | MEMS microphone leaders |
| 4.12. | Voice System on Chip for Terminals |
| 4.13. | Voice SoC features |
| 4.14. | AI Voice SoC |
| 4.15. | From voice to voice AI SoC |
| 4.16. | Evolution of SoC for voice assistant technologies |
| 4.17. | Voice SoC companies |
| 4.18. | UniOne |
| 4.19. | Hangzhou Guoxin Technology |
| 4.20. | MIT's low-power chip for speech recognition |
| 4.21. | Artificial Intelligence and Deep Learning |
| 4.22. | From artificial intelligence, to machine learning and deep learning |
| 4.23. | Artificial intelligence in the development of human-machine interactions |
| 4.24. | Terminologies and scopes |
| 4.25. | Things improved deep learning |
| 4.26. | Rising interest in google trends |
| 4.27. | An artificial neuron in the training process |
| 4.28. | Artificial neural network |
| 4.29. | Deep learning |
| 4.30. | The age of gradient descent |
| 4.31. | Main varieties of machine learning approaches |
| 4.32. | Evolution of deep learning |
| 4.33. | Dialogue Systems |
| 4.34. | Types of dialogue systems |
| 4.35. | Spoken dialogue system processes |
| 4.36. | Development stage of speech processing technologies |
| 4.37. | Front-End Signal Processing |
| 4.38. | Front-end processing for speech recognition |
| 4.39. | Voice activity detection |
| 4.40. | Acoustic echo cancellation |
| 4.41. | Dereverberation |
| 4.42. | Beamforming |
| 4.43. | Sensors for voice biometrics: VocalZoom |
| 4.44. | VocalZoom used in cars |
| 4.45. | Humidity sensor with carbon nanotubes for biometric sensing |
| 4.46. | Algorithm-based approach |
| 4.47. | Keyword Spotting (KWS) |
| 4.48. | Keyword spotting |
| 4.49. | LVCSR KWS |
| 4.50. | Acoustic KWS |
| 4.51. | Phonetic search KWS |
| 4.52. | Automatic Speech Recognition (ASR) |
| 4.53. | Speech recognition |
| 4.54. | Timeline of language technologies |
| 4.55. | Approaches to and types of speech recognition |
| 4.56. | Evolution of speech recognition |
| 4.57. | Modern speech recognition processes |
| 4.58. | Feature extraction methods |
| 4.59. | Challenges in speech recognition |
| 4.60. | Speech technology of Baidu: roadmap of speech recognition in Baidu |
| 4.61. | Natural Language Processing (NLP) and Natural Language Understanding (NLU) |
| 4.62. | Natural language processing and natural language understanding |
| 4.63. | Levels of linguistic analyses |
| 4.64. | Natural language understanding |
| 4.65. | Natural language understanding system |
| 4.66. | Knowledge sources for speech understanding |
| 4.67. | Text-To-Speech (TTS) |
| 4.68. | Text-to-speech system |
| 4.69. | Amazon's "Polly" synthesiser |
| 4.70. | DeepMind of google |
| 4.71. | VoicePrint Recognition (VPR) |
| 4.72. | Different voice/sound prints |
| 4.73. | Voiceprint recognition |
| 4.74. | Speech recognition vs. voice recognition |
| 4.75. | Challenges |
| 4.76. | Voice recognition process |
| 4.77. | VPR procedure |
| 4.78. | Information security |
| 4.79. | Biometrics in finance |
| 4.80. | New Zealand government using voice biometrics for telephone system |
| 4.81. | Siri of Apple |
| 4.82. | Representative players |
| 4.83. | Emotion detection |
| 4.84. | Machine Translation |
| 4.85. | Translation approaching human level performance |
| 4.86. | Machine translation |
| 4.87. | Speech translation |
| 4.88. | Microsoft: deep learning for machine translation |
| 5. | VERTICAL APPLICATIONAL MARKETS AND RELEVANT PLAYERS |
| 5.1. | Speech UI enables many applications |
| 5.2. | Role of speech in different devices |
| 5.3. | Applications |
| 5.4. | Dictation |
| 5.5. | Information security |
| 5.6. | Interactive voice response |
| 5.7. | IVR value propositions |
| 5.8. | IVR case studies |
| 5.9. | Automotive |
| 5.10. | Speech-user-interface-enabled functions for automotive |
| 5.11. | Development roadmap of speech UI in automotive |
| 5.12. | Speech-based in-vehicle system case studies |
| 5.13. | Speech recognition used in intoxication measurements |
| 5.14. | Banking, Financial services and Insurance (BFSI) |
| 5.15. | Healthcare and life sciences |
| 5.16. | Speech translation device |
| 5.17. | Healthcare apps using Amazon Alexa |
| 5.18. | Health information at home through voice technology |
| 5.19. | Hospitals look to Amazon Alexa |
| 5.20. | Alexa-powered AI genomics platform |
| 5.21. | Travel, hotels |
| 5.22. | Retails/commerce |
| 5.23. | Home automation |
| 5.24. | Education |
| 5.25. | iFlytek's product portfolio |
| 5.26. | Game & entertainment |
| 5.27. | TV solutions |
| 5.28. | Robotics |
| 5.29. | Virtual personal assistant |
| 5.30. | Towards VPA |
| 5.31. | Conversational interaction illustration for VPAs |
| 5.32. | Exploring Business models for virtual personal assistants |
| 5.33. | Siri of Apple |
| 5.34. | Evolution of iPhone's speech user interface |
| 5.35. | VocalIQ |
| 5.36. | Future Siri |
| 5.37. | Microsoft Cortana |
| 5.38. | Technologies involved with Cortana |
| 5.39. | IBM Watson |
| 5.40. | Preparation for Watson: partnerships and acquisitions |
| 5.41. | A list of virtual assistants |
| 5.42. | Comparison of intelligent virtual assistants |
| 5.43. | Open access of Google SR API and AudioSet |
| 5.44. | Viv |
| 5.45. | Chatbot |
| 5.46. | Messaging interfaces of chatbots |
| 5.47. | Facebook's M |
| 5.48. | Bot platforms with AI |
| 5.49. | Virtual idol enabled by speech synthesis |
| 5.50. | Revenue models of Vocaloid |
| 5.51. | Wearables |
| 5.52. | Intel: from Javis to Radar Pace |
| 5.53. | Kopin's voice interface |
| 5.54. | Whisper™ Chip |
| 6. | PLAYERS |
| 6.1. | The contestants |
| 6.2. | Case study: The decline and reposition of Nuance—the formerly leader in speech |
| 6.3. | Lists of players in the value chain and technology offerings |
| 7. | COMPANY PROFILES |
| 7.1. | AISpeech |
| 7.2. | Amazon (Alexa) |
| 7.3. | Beijing Kexin Technology |
| 7.4. | d-Ear Technologies |
| 7.5. | iFlyTek |
| 7.6. | MindMeld |
| 7.7. | Next IT Corporation |
| 7.8. | Nuance Communications |
| 7.9. | Unisound |
The market for smart speech/voice-based technology will reach $ 15.5 billion by 2029
Report Statistics
| Slides | 313 |
|---|---|
| Forecasts to | 2029 |
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