Dublin, May 06, 2022 (GLOBE NEWSWIRE) -- The "Global Speech-to-text API Market Size, Share & Industry Trends Analysis Report By Component (Solution and Services), By Vertical, By Organization Size, By Deployment Type, By Application, By Regional Outlook and Forecast, 2021-2027" report has been added to ResearchAndMarkets.com's offering.
The Global Speech-to-text API Market size is expected to reach $5.8 billion by 2027, rising at a market growth of 19.0% CAGR during the forecast period.
The speech-to-text application programming interface (API) is a programming interface that enables the utilization of speech synthesis and recognition in a variety of devices and applications. Speech-to-text API is a multidisciplinary subject of computational linguistics that explores methods that allow computers to translate and recognize audible language into text. This is also called as Automatic Speech Recognition (ASR) or Speech-to-Text.
It encompasses electrical engineering, computer science, and linguistics research and knowledge. Deep learning and big data advancements have aided the field in recent years. The progress is evidenced not only by the rapid increase in the number of academic papers published in the subject but also by the widespread industry use of a range of deep learning approaches in the design and implementation of voice recognition systems around the world.
Any video or audio-based information can be captioned and subtitled using the speech-to-text API technology, allowing struggling listeners or learners with visual impairments to understand and complete their work without assistance. Speech-to-text APIs, for example, can help students with hearing loss communicate with their teachers and peers. However, the key obstacles in the speech-to-text API market are multilingual support for captioning and subtitling, as well as establishing unique vocabulary across multiple verticals.
COVID-19 Impact Analysis
Many organizations witnessed increased consumer pressure during the pandemic, while their number of available workers was reduced. Many contact centers were unable to meet demand or were forced to close due to lockdown restrictions, resulting in high wait times for customer service requests and a negative impact on the customer experience. Speech-to-text API is moving to the forefront of technology enablers as companies adopt a more strategic strategy that offers resilience into operations through flexibility and scalability while also working to increase operational efficiencies.
Medical speech recognition capabilities are sought by data analytics application developers to assist them swiftly and accurately transcribing video and audio incorporating COVID-19 terminology into text for downstream analytics. Amazon Transcribe Medical, for example, is a fully managed speech recognition (ASR) service that makes it simple to add medical speech-to-text capabilities to any application.
Market Growth Factors:
The massive penetration of smartphones is creating the requirement for voice-based devices
With the widespread acceptance of technology and the vast development of internet-based material, the demand for smart devices such as smart speakers and mobile phones has increased over the last decade, resulting in a greater need to make online video content available to everyone. Several new advanced gadgets with voice-controlled functions, such as content transcription and conference call analysis, are being introduced, allowing consumers to access educational, entertainment, and other information via their smart devices. As a result of the rising requirement to understand client preferences, speech-to-text apps have grown in popularity.
Several organizations collect client data about media material and translate it into texts to assist content providers in determining what types of content are acceptable and becoming more popular. Moreover, the demand for smart homes and smart appliances is rising as a result of a number of factors, including rising internet penetration, technological improvements, and increased awareness of automation.
The growing number of advanced speech-to-text solutions for differently-abled students
Any video or audio-based content can be translated by a computer into text using the speech-to-text API technology, which allows struggling listeners or hard-of-hearing students read appropriately and complete their work without the assistance of others. Speech-to-text software, for example, can help a deaf-mute student interact with his or her professors and classmates. As a result, this system functions as assistive technology, allowing impaired persons to take advantage of ICT. For impaired students, the Individuals with Disabilities Education Act (IDEA) provides interactive software. In the classroom, these students are unable to hear well.
To address this, professors at Northern Illinois University, created an interactive software lesson that uses speech-to-text technology to assist these students in learning the Nemeth code (a Braille code for mathematics).
Marketing Restraining Factor:
Transcribing audio from many channels could stymie the market for speech-to-text APIs.
Transcribing audio from numerous channels is a significant barrier for this technology since defining many things becomes challenging, resulting in erroneous transcriptions or captions. In addition, background noise, low-quality microphones, reverb and echo, and accent changes all have the potential to degrade transcription accuracy.
Voice-to-text APIs should be appropriately trained for multi-channel speech recognition using a number of data sets; however, gathering a variety of data sets for establishing an approach and solution that accurately converts speech-to-text for many channels can be problematic for businesses. Moreover, privacy concerns about voice-enabled gadgets is expected to discourage many entities to embrace these solutions.
Key Topics Covered:
Chapter 1. Market Scope & Methodology
Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market Composition and Scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints
Chapter 3. Competition Analysis - Global
3.1 KBV Cardinal Matrix
3.2 Recent Industry Wide Strategic Developments
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisition and Mergers
3.3 Top Winning Strategies
3.3.1 Key Leading Strategies: Percentage Distribution (2017-2021)
3.3.2 Key Strategic Move: (Product Launches and Product Expansions : 2017, Mar - 2021, Nov) Leading Players
Chapter 4. Global Speech-to-text API Market by Component
4.1 Global Solution Market by Region
4.2 Global Services Market by Region
Chapter 5. Global Speech-to-text API Market by Vertical
5.1 Global BFSI Market by Region
5.2 Global IT & Telecom Market by Region
5.3 Global Healthcare Market by Region
5.4 Global Retail & eCommerce Market by Region
5.5 Global Government & Defense Market by Region
5.6 Global Media & Entertainment Market by Region
5.7 Global Travel & Hospitality Market by Region
5.8 Global Others Market by Region
Chapter 6. Global Speech-to-text API Market by Organization Size
6.1 Global Large Enterprises Market by Region
6.2 Global Small & Medium-sized Enterprises (SMEs) Market by Region
Chapter 7. Global Speech-to-text API Market by Deployment Type
7.1 Global Cloud Market by Region
7.2 Global On-premise Market by Region
Chapter 8. Global Speech-to-text API Market by Application
8.1 Global Fraud Detection & Prevention Market by Region
8.2 Global Contact Center & Customer Management Market by Region
8.3 Global Risk & Compliance Management Market by Region
8.4 Global Content Transcription Market by Region
8.5 Global Subtitle Generation Market by Region
8.6 Global Others Market by Region
Chapter 9. Global Speech-to-text API Market by Region
Chapter 10. Company Profiles
10.1 LivePerson, Inc. (VoiceBase, Inc.)
10.1.1 Company Overview
10.1.2 Financial Analysis
10.1.3 Regional & Segmental Analysis
10.2 VoiceCloud LLC
10.2.1 Company Overview
10.3 Speechmatics Ltd.
10.3.1 Company Overview
10.3.2 Recent strategies and developments:
10.3.2.1 Partnerships, Collaborations, and Agreements:
10.3.2.2 Product Launches and Product Expansions:
10.4 IBM Corporation
10.4.1 Company Overview
10.4.2 Financial Analysis
10.4.3 Regional & Segmental Analysis
10.4.4 Research & Development Expenses
10.4.5 Recent strategies and developments:
10.4.5.1 Product Launches and Product Expansions:
10.5 Microsoft Corporation
10.5.1 Company Overview
10.5.2 Financial Analysis
10.5.3 Segmental and Regional Analysis
10.5.4 Research & Development Expenses
10.5.5 Recent strategies and developments:
10.5.5.1 Partnerships, Collaborations, and Agreements:
10.5.5.2 Product Launches and Product Expansions:
10.6 Google LLC
10.6.1 Company Overview
10.6.2 Financial Analysis
10.6.3 Segmental and Regional Analysis
10.6.4 Research & Development Expense
10.6.5 Recent strategies and developments:
10.6.6 SWOT Analysis
10.7 Baidu, Inc.
10.7.1 Company Overview
10.7.2 Financial Analysis
10.7.3 Segmental Analysis
10.7.4 Research & Development Expenses
10.7.5 Recent strategies and developments:
10.7.5.1 Product Launches and Product Expansions:
10.7.5.2 Acquisition and Mergers
10.7.6 SWOT Analysis
10.8 Twilio, Inc.
10.8.1 Company Overview
10.8.2 Financial Analysis
10.8.3 Regional Analysis
10.8.4 Research & Development Expense
10.8.5 Recent strategies and developments:
10.9 Amazon Web Services, Inc.
10.9.1 Company Overview
10.9.2 Financial Analysis
10.9.3 Segmental and Regional Analysis
10.9.4 Recent strategies and developments:
10.9.4.1 Partnerships, Collaborations, and Agreements:
10.9.4.2 Product Launches and Product Expansions:
10.10. Verint Systems, Inc.
10.10.1 Company Overview
10.10.2 Financial Analysis
10.10.3 Segmental and Regional Analysis
10.10.4 Research and Development Expense
10.10.5 Recent strategies and developments:
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