Global Text-to-Speech API Industry Expected to Reach $5.8 Billion by 2027

DUBLIN, May 9, 2022 /PRNewswire/ — The “Global Speech-to-text API Market Size, Share and Trend 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 from ResearchAndMarkets.com offer.

The Global Speech-to-text API Market Size is Expected to Reach $5.8 billion by 2027, with the market growing at 19.0% CAGR during the forecast period.

The Text-to-Speech Application Programming Interface (API) is a programming interface that enables the use of text-to-speech and recognition in a variety of devices and applications. The Speech-to-text API is a multidisciplinary subject of computational linguistics that explores methods by which computers can translate and recognize audible language into text. This is also called Automatic Speech Recognition (ASR) or Speech-to-Text.

It encompasses research and knowledge in electrical engineering, computer science and linguistics. Deep learning and advancements in Big Data have helped the field in recent years. Progress is evidenced not only by the rapid increase in the number of academic papers published on the subject, but also by the widespread use by industry of a range of deep learning approaches in design and implementation of speech recognition systems worldwide.

Any video or audio information can be captioned and captioned using text-to-speech API technology, allowing struggling listeners or visually impaired learners to understand and complete their work without assistance. Text-to-speech APIs, for example, can help students with hearing loss communicate with teachers and peers. However, the major hurdles in the text-to-speech API market are multilingual support for captioning and captioning, as well as establishing a single vocabulary across multiple verticals.

COVID-19 Impact Analysis

Many organizations have witnessed increased consumer pressure during the pandemic, while their number of available workers has been reduced. Many contact centers were unable to keep up with demand or were forced to close due to lockdown restrictions, leading to high wait times for customer service inquiries and a negative impact on the customer experience. Text-to-speech API is moving to the forefront of technology enablers as enterprises adopt a more strategic strategy that delivers resilience to operations through flexibility and scalability while striving to increase operational efficiency.

Data analytics app developers are looking for medical speech recognition capabilities to help them quickly and accurately transcribe video and audio incorporating COVID-19 terminology into text for downstream analysis. Amazon Transcribe Medical, for example, is a fully managed speech recognition (ASR) service that makes it easy to add medical text-to-speech capabilities to any application.

Market Growth Factors:

Massive Smartphone Penetration Creates the Need for Voice Devices

With the widespread acceptance of technology and the vast development of internet-based hardware, the demand for smart devices such as smart speakers and mobile phones has increased over the past decade, resulting in a need to make online video content accessible to everyone. Several new advanced gadgets with voice-enabled functions, such as content transcription and conference call analysis, are being introduced, allowing consumers to access educational, entertainment and other information through their smart devices. Due to the growing need to understand customer preferences, text-to-speech applications have grown in popularity.

Several organizations collect customer data on media material and translate it into text to help content providers determine what types of content are acceptable and becoming more popular. Additionally, the demand for smart homes and smart appliances is increasing due to a number of factors including growing internet penetration, technological improvements, and increased awareness of automation.

The growing number of advanced text-to-speech solutions for students with disabilities

Any video or audio content can be translated by a computer into text using text-to-speech API technology, which allows struggling listeners or hearing-impaired students to read correctly and complete their work without assistance from others . Text-to-speech software, for example, can help a deaf-mute student interact with teachers and classmates. Therefore, this system functions as an assistive technology, enabling people with disabilities to benefit from ICT. For students with disabilities, the Individuals with Disabilities Education Act (IDEA) provides interactive software. In class, these students do not hear well.

To remedy this, the teachers of Northern Illinois Universitycreated an interactive software lesson that uses text-to-speech technology to help these students learn the Nemeth code (a braille code for math).

Marketing Restriction Factor:

Audio transcription of many channels could hamper the text-to-speech API market.

Transcribing audio from many channels is a significant hurdle for this technology, as defining many elements becomes difficult, resulting in erroneous transcriptions or subtitles. Additionally, background noise, poor quality microphones, reverb and echo, and accent changes all have the potential to degrade transcription accuracy.

Voice-to-text APIs must be properly trained for multi-channel speech recognition using a number of data sets; However, collecting a variety of data sets to establish an approach and solution that accurately converts speech to text for many channels can be problematic for businesses. Additionally, privacy concerns regarding voice-enabled gadgets are expected to discourage many entities from adopting these solutions.

Main topics covered:

Chapter 1. Market Scope and Methodology

Chapter 2. Market Overview
2.1 Presentation
2.1.1 Presentation
2.1.1.1 Market composition and scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market constraints

Chapter 3. Competitive Analysis – Global
3.1 Cardinal Matrix KBV
3.2 Recent Industry-Wide Strategic Developments
3.2.1 Partnerships, collaborations and agreements
3.2.2 Product launches and product extensions
3.2.3 Acquisitions and mergers
3.3 Main winning strategies
3.3.1 Key Primary Strategies: Percentage Breakdown (2017-2021)
3.3.2 Key Strategic Movement: (Product Launches and Product Extensions: 2017, Mar – 2021, Nov) Key Players

Chapter 4. Global Speech-to-text API Market by Component
4.1 Global Solutions 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 Information and Telecommunications Technology Market by Region
5.3 Global Healthcare Market by Region
5.4 Global Retail and e-Commerce Market by Region
5.5 Global Government and Defense Market by Region
5.6 Global Media and Entertainment Market by Region
5.7 Global Travel and 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 Enterprise 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-premises Market by Region

Chapter 8. Global Speech-to-text API Market by Application
8.1 Global Fraud Detection and Prevention Market by Region
8.2 Global Contact Center and Customer Management Market by Region
8.3 Global Risk and Compliance Management Market by Region
8.4 Global Content Transcription Market by Region
8.5 Global Caption Generation Market by Region
8.6 Global Others Market by Region

Chapter 9. Global Speech-to-text API Market by Region

Chapter 10. Business Profiles
10.1 LivePerson, Inc. (VoiceBase, Inc.)
10.1.1 Company Overview
10.1.2 Financial analysis
10.1.3 Regional and Sector 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 extensions:
10.4 IBM Corporation
10.4.1 Company Overview
10.4.2 Financial analysis
10.4.3 Regional and Sector Analysis
10.4.4 Research and development costs
10.4.5 Recent Strategies and Developments:
10.4.5.1 Product launches and product extensions:
10.5 Microsoft Corporation
10.5.1 Company Overview
10.5.2 Financial analysis
10.5.3 Sectoral and regional analysis
10.5.4 Research and development costs
10.5.5 Recent Strategies and Developments:
10.5.5.1 Partnerships, collaborations and agreements:
10.5.5.2 Product launches and product extensions:
10.6 Google LLC
10.6.1 Company Overview
10.6.2 Financial analysis
10.6.3 Sectoral and regional analysis
10.6.4 Research and development costs
10.6.5 Strategies and Recent 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 and development costs
10.7.5 Recent Strategies and Developments:
10.7.5.1 Product launches and product extensions:
10.7.5.2 Acquisition and merger
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 and development costs
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 Sectoral and regional analysis
10.9.4 Strategies and Recent Developments:
10.9.4.1 Partnerships, collaborations and agreements:
10.9.4.2 Product launches and product extensions:
10.10. Verint Systems, Inc.
10.10.1 Company Overview
10.10.2 Financial Analysis
10.10.3 Sectoral and regional analysis
10.10.4 Research and development costs
10.10.5 Strategies and Recent Developments:

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