Sensors & Haptics Report

The market for smart speech/voice-based technology will reach $ 15.5 billion by 2029

Voice, Speech, Conversation-Based User Interfaces 2019-2029: Technologies, Players, Markets

Smart Voice, Smart Speakers, Voice Assistant, Voice-Enabled User Interface

Brand new for October 2018

Table of Contents
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
2.1.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
3.1.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
3.12.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
4.1.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
4.19.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
4.43.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
4.50.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
4.76.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
5.1.Speech UI enables many applications
5.2.Role of speech in different devices
5.5.Information security
5.6.Interactive voice response
5.7.IVR value propositions
5.8.IVR case studies
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
5.23.Home automation
5.25.iFlytek's product portfolio
5.26.Game & entertainment
5.27.TV solutions
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
5.36.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
5.46.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
5.52.Intel: from Javis to Radar Pace
5.53.Kopin's voice interface
5.54.Whisper™ Chip
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
7.2.Amazon (Alexa)
7.3.Beijing Kexin Technology
7.4.d-Ear Technologies
7.7.Next IT Corporation
7.8.Nuance Communications

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