Automatic Voice & Speech Recognition Software Market by Technology (Machine Learning-based Recognition, Deep Learning-based Recognition, Natural Language Processing (NLP)), Deployment Type (Cloud-Based, On-Premise), Function, Application, End-User Industr
Description
The Automatic Voice & Speech Recognition Software Market was valued at USD 22.01 billion in 2024 and is projected to grow to USD 26.20 billion in 2025, with a CAGR of 18.98%, reaching USD 88.46 billion by 2032.
A concise orientation to how recent model breakthroughs and operational constraints are reshaping adoption pathways for voice and speech recognition across industries
Automatic voice and speech recognition technologies have transformed from niche experimental systems into mission-critical components across customer experience, clinical documentation, and embedded device interfaces. Advances in deep learning architectures and the integration of transformer-based language models have materially improved transcription accuracy, speaker separation, and multilingual capability, enabling broader deployment across real-time and batch use cases. At the same time, growing expectations for privacy, explainability, and low-latency performance have driven new engineering trade-offs that require careful alignment between product, data, and infrastructure teams.
This executive summary synthesizes the most consequential trends shaping the industry, identifies systemic risks and opportunity vectors, and delivers pragmatic guidance for leaders who must prioritize investments without compromising regulatory compliance or user trust. Throughout the analysis, emphasis is placed on how technological progress, procurement patterns, and global trade dynamics interact to influence adoption pathways. By the end of this section, decision-makers will have a clear orientation on where to focus resources to achieve measurable business outcomes while maintaining governance and resiliency.
How model advances, on-device inference, and privacy-first architectures are converging to rewrite product strategies and ecosystem partnerships in voice technology
The landscape of automatic voice and speech recognition is undergoing transformative shifts driven by confluence of foundational model innovation, distributed compute, and changing expectations for privacy and robustness. Transformer architectures and self-supervised learning have enhanced model generalization, enabling systems to support a widening array of languages, dialects, and acoustic environments. Concurrently, edge compute and specialized inference accelerators have reduced latency and offloaded sensitive audio processing from centralized clouds, enabling real-time applications in automobiles, wearables, and industrial systems.
As a result, product roadmaps are increasingly hybrid: deploying smaller, task-tuned models at the edge for low-latency interactions while leveraging larger, centralized models for complex contextual understanding and analytics. This hybrid approach dovetails with renewed emphasis on data governance; manufacturers and operators are prioritizing techniques such as on-device anonymization, federated learning, and differential privacy to reconcile personalization with regulatory constraints. In parallel, the competitive landscape has broadened, with cloud providers, semiconductor vendors, and specialized startups each staking differentiated claims on the value chain, shaping partnership and M&A activity across ecosystems.
The cumulative effects of US trade measures in 2025 forced rapid supply chain diversification and hardware architecture changes that shifted procurement and engineering priorities
The introduction and escalation of tariffs and trade measures in the United States during 2025 created a material re-evaluation of global supply chains and procurement strategies for hardware-dependent voice and speech recognition solutions. Tariff-driven cost pressures affected the sourcing of key components such as microphones, MEMS sensors, accelerators, and networking hardware, prompting companies to re-assess vendor contracts and inventory buffers. This environment encouraged buyers and vendors to accelerate diversification of supplier bases and to re-specify designs to reduce dependency on tariffed components.
Consequently, engineering teams shifted toward modular hardware architectures and open standards to simplify substitution, while procurement leadership prioritized dual-sourcing and nearshoring options to mitigate border risk. These adaptations also catalyzed strategic decisions about deployment mode; some organizations increased investment in software-defined approaches and virtualized inference to reduce reliance on specialized hardware, whereas others pursued localized manufacturing partnerships to retain competitive performance characteristics. In sum, tariffs acted as a catalyst for resilience planning, driving structural changes in sourcing, architecture, and commercial contracting that will persist beyond the immediate trade measures.
A layered segmentation framework that links application nuance, component roles, deployment modes, and end-user vertical requirements to guide procurement and product strategy
A clear segmentation lens is essential to translate technology capability into business impact, and this market is best understood through layered perspectives that connect application, component, deployment mode, and end user. Application-level differentiation matters because call center automation, dictation and transcription, virtual assistants, and voice biometrics each impose distinct requirements on latency, accuracy, and security. Within dictation and transcription, the needs for general transcription differ from those in legal transcription and medical transcription where specialized vocabularies, compliance constraints, and audit trails become determinative. Similarly, virtual assistants split into customer service assistants and personal assistants, where context retention and task orchestration vary substantially.
Component segmentation clarifies investment priorities across hardware, services, and software. Services themselves require nuance; consulting, integration and deployment, and support and maintenance are not interchangeable and demand separate governance. Deployment mode, whether cloud or on-premise, influences design choices: cloud options may include hybrid cloud, private cloud, and public cloud, each presenting trade-offs around control and scalability. End user verticals further shape requirements: automotive and transportation applications span in-vehicle systems and traffic management; BFSI covers banking, capital markets, and insurance; healthcare encompasses home healthcare, hospitals and clinics, and telehealth; retail and e-commerce includes e-commerce customer support and in-store assistance; and telecom and IT focuses on customer service and network management. These combined segmentation layers provide the analytic scaffolding necessary to evaluate vendors, architecture choices, and commercialization approaches in a way that links technical trade-offs to sector-specific objectives.
How regional regulatory regimes, language complexity, and infrastructure maturity across the Americas, Europe, Middle East & Africa, and Asia-Pacific determine differentiated adoption pathways
Regional dynamics continue to shape technology adoption, regulatory expectations, and partnership models across the Americas, Europe, Middle East & Africa, and Asia-Pacific, each presenting distinct demand drivers and implementation constraints. In the Americas, deployments emphasize rapid commercialization and tight integration with cloud ecosystems, often driven by customer experience and enterprise productivity goals. This region favors flexible consumption models, strong developer ecosystems, and early adoption of voice-enabled automation in customer-facing operations.
In Europe, Middle East & Africa, regulatory frameworks and multilingual complexity lead to conservative but strategic adoption patterns, with organizations prioritizing data sovereignty, privacy safeguards, and strong localization support. Firms in this region frequently opt for hybrid architectures that align with national data residency requirements and multilingual model adaptations. In Asia-Pacific, high mobile penetration and a diversity of languages and dialects accelerate innovation in on-device inference and low-cost hardware integration, particularly in consumer electronics and telecommunication scenarios. Regional partnerships and localized model training are common approaches to solving language and dialect coverage at scale. Collectively, these geographic distinctions inform go-to-market models, partnership decisions, and the prioritization of technical capabilities for vendors and integrators.
How platform providers, semiconductor innovators, and vertical specialists are aligning product, partnerships, and commercial models to capture differentiated value in speech solutions
Leading companies in the automatic voice and speech recognition ecosystem are executing divergent strategies to capture value across platforms, silicon, and services. Some vendors focus on full-stack solutions that integrate cloud-based training pipelines, managed inference services, and enterprise-grade security, targeting customers who prioritize rapid deployment and ongoing model improvements. By contrast, semiconductor and hardware suppliers emphasize acceleration and power-efficiency to enable high-performance on-device inference for automotive and embedded devices, partnering closely with software vendors to co-optimize stacks.
A third cohort of companies pursues specialization: offering best-in-class vertical solutions for healthcare transcription, legal compliance, or voice biometrics, often combining curated language models with domain-specific lexicons and annotation pipelines. Across these archetypes, successful players invest in robust developer tooling, open APIs, and transparent performance benchmarking to lower integration friction. Strategic alliances and selective acquisition activity tend to focus on filling capability gaps-particularly in multilingual modeling, noise robustness, and real-time speaker diarization-while pricing models evolve toward outcome-based contracting and consumption-aligned licensing to better match customer ROI expectations.
Practical recommendations for balancing edge performance, data governance, vendor flexibility, and ecosystem partnerships to accelerate reliable voice technology adoption
Industry leaders should adopt a pragmatic, multi-pronged approach that balances near-term delivery with long-term resilience to capture the strategic upside of voice technologies. First, they should prioritize hybrid deployment architectures that allow critical processing to occur at the edge where latency and privacy matter, while retaining centralized capabilities for continuous learning and analytics. Second, investment in data governance and annotation pipelines is essential to ensure domain accuracy and to prevent model drift; organizations should design feedback loops that incorporate human-in-the-loop validation for high-stakes contexts.
Next, procurement teams must reframe vendor selection to include flexibility for hardware substitution and software portability, negotiating contracts that allow modular upgrades and clear service-level commitments. Additionally, firms should develop competency in model evaluation metrics beyond raw accuracy, incorporating robustness to background noise, speaker variability, and adversarial inputs. Finally, leaders should pursue ecosystem partnerships strategically-partnering with semiconductor suppliers, systems integrators, and domain specialists to co-develop reference implementations and to accelerate time-to-value while sharing risk and IP.
A multi-method research approach combining practitioner interviews, technical assessment, and scenario-based validation to ensure robust and actionable insights
This research synthesizes insights from a multi-method approach combining primary engagement with industry practitioners and secondary analysis of technical literature, standards, and publicly available corporate disclosures. Primary methods included structured interviews with product leaders, system architects, procurement executives, and regulatory experts to capture lived implementation challenges and vendor selection criteria. These interviews were complemented by technical assessments of model architectures, inference frameworks, and edge computing platforms to understand trade-offs in latency, accuracy, and resource consumption.
Secondary analysis involved systematic review of academic and industry publications on recent model innovations, privacy-preserving techniques, and hardware accelerators, followed by triangulation of findings across sources. Data quality was enhanced through cross-validation with deployment case studies and vendor technical documentation. Finally, analytic conclusions were stress-tested using scenario analysis that considered variations in regulatory regimes, supply chain disruptions, and technology maturation timelines to ensure recommendations remain robust under different plausible futures.
A strategic synthesis emphasizing hybrid architectures, rigorous governance, and iterative deployments to maximize business value from voice and speech technologies
In conclusion, automatic voice and speech recognition technologies have entered a phase where technical maturity meets practical deployment complexity, requiring leaders to make deliberate choices about architecture, sourcing, and governance. The pace of model innovation enables richer, more natural interactions and broader language coverage, but it also imposes new responsibilities around privacy, fairness, and resiliency. Organizations that adopt hybrid architectures, invest in rigorous annotation and evaluation pipelines, and cultivate flexible supplier relationships will be positioned to extract disproportionate value from voice-enabled experiences.
Looking ahead, success will depend on the ability to operationalize continuous learning while maintaining transparent controls and user-centric privacy safeguards. By aligning technical investments with sector-specific requirements and by adopting an iterative deployment approach that learns from early pilots, enterprises can reduce risk and accelerate measurable business outcomes. These strategic imperatives should guide executive sponsorship, budgeting, and cross-functional coordination as voice technologies become increasingly integral to customer experiences and operational workflows.
Please Note: PDF & Excel + Online Access - 1 Year
A concise orientation to how recent model breakthroughs and operational constraints are reshaping adoption pathways for voice and speech recognition across industries
Automatic voice and speech recognition technologies have transformed from niche experimental systems into mission-critical components across customer experience, clinical documentation, and embedded device interfaces. Advances in deep learning architectures and the integration of transformer-based language models have materially improved transcription accuracy, speaker separation, and multilingual capability, enabling broader deployment across real-time and batch use cases. At the same time, growing expectations for privacy, explainability, and low-latency performance have driven new engineering trade-offs that require careful alignment between product, data, and infrastructure teams.
This executive summary synthesizes the most consequential trends shaping the industry, identifies systemic risks and opportunity vectors, and delivers pragmatic guidance for leaders who must prioritize investments without compromising regulatory compliance or user trust. Throughout the analysis, emphasis is placed on how technological progress, procurement patterns, and global trade dynamics interact to influence adoption pathways. By the end of this section, decision-makers will have a clear orientation on where to focus resources to achieve measurable business outcomes while maintaining governance and resiliency.
How model advances, on-device inference, and privacy-first architectures are converging to rewrite product strategies and ecosystem partnerships in voice technology
The landscape of automatic voice and speech recognition is undergoing transformative shifts driven by confluence of foundational model innovation, distributed compute, and changing expectations for privacy and robustness. Transformer architectures and self-supervised learning have enhanced model generalization, enabling systems to support a widening array of languages, dialects, and acoustic environments. Concurrently, edge compute and specialized inference accelerators have reduced latency and offloaded sensitive audio processing from centralized clouds, enabling real-time applications in automobiles, wearables, and industrial systems.
As a result, product roadmaps are increasingly hybrid: deploying smaller, task-tuned models at the edge for low-latency interactions while leveraging larger, centralized models for complex contextual understanding and analytics. This hybrid approach dovetails with renewed emphasis on data governance; manufacturers and operators are prioritizing techniques such as on-device anonymization, federated learning, and differential privacy to reconcile personalization with regulatory constraints. In parallel, the competitive landscape has broadened, with cloud providers, semiconductor vendors, and specialized startups each staking differentiated claims on the value chain, shaping partnership and M&A activity across ecosystems.
The cumulative effects of US trade measures in 2025 forced rapid supply chain diversification and hardware architecture changes that shifted procurement and engineering priorities
The introduction and escalation of tariffs and trade measures in the United States during 2025 created a material re-evaluation of global supply chains and procurement strategies for hardware-dependent voice and speech recognition solutions. Tariff-driven cost pressures affected the sourcing of key components such as microphones, MEMS sensors, accelerators, and networking hardware, prompting companies to re-assess vendor contracts and inventory buffers. This environment encouraged buyers and vendors to accelerate diversification of supplier bases and to re-specify designs to reduce dependency on tariffed components.
Consequently, engineering teams shifted toward modular hardware architectures and open standards to simplify substitution, while procurement leadership prioritized dual-sourcing and nearshoring options to mitigate border risk. These adaptations also catalyzed strategic decisions about deployment mode; some organizations increased investment in software-defined approaches and virtualized inference to reduce reliance on specialized hardware, whereas others pursued localized manufacturing partnerships to retain competitive performance characteristics. In sum, tariffs acted as a catalyst for resilience planning, driving structural changes in sourcing, architecture, and commercial contracting that will persist beyond the immediate trade measures.
A layered segmentation framework that links application nuance, component roles, deployment modes, and end-user vertical requirements to guide procurement and product strategy
A clear segmentation lens is essential to translate technology capability into business impact, and this market is best understood through layered perspectives that connect application, component, deployment mode, and end user. Application-level differentiation matters because call center automation, dictation and transcription, virtual assistants, and voice biometrics each impose distinct requirements on latency, accuracy, and security. Within dictation and transcription, the needs for general transcription differ from those in legal transcription and medical transcription where specialized vocabularies, compliance constraints, and audit trails become determinative. Similarly, virtual assistants split into customer service assistants and personal assistants, where context retention and task orchestration vary substantially.
Component segmentation clarifies investment priorities across hardware, services, and software. Services themselves require nuance; consulting, integration and deployment, and support and maintenance are not interchangeable and demand separate governance. Deployment mode, whether cloud or on-premise, influences design choices: cloud options may include hybrid cloud, private cloud, and public cloud, each presenting trade-offs around control and scalability. End user verticals further shape requirements: automotive and transportation applications span in-vehicle systems and traffic management; BFSI covers banking, capital markets, and insurance; healthcare encompasses home healthcare, hospitals and clinics, and telehealth; retail and e-commerce includes e-commerce customer support and in-store assistance; and telecom and IT focuses on customer service and network management. These combined segmentation layers provide the analytic scaffolding necessary to evaluate vendors, architecture choices, and commercialization approaches in a way that links technical trade-offs to sector-specific objectives.
How regional regulatory regimes, language complexity, and infrastructure maturity across the Americas, Europe, Middle East & Africa, and Asia-Pacific determine differentiated adoption pathways
Regional dynamics continue to shape technology adoption, regulatory expectations, and partnership models across the Americas, Europe, Middle East & Africa, and Asia-Pacific, each presenting distinct demand drivers and implementation constraints. In the Americas, deployments emphasize rapid commercialization and tight integration with cloud ecosystems, often driven by customer experience and enterprise productivity goals. This region favors flexible consumption models, strong developer ecosystems, and early adoption of voice-enabled automation in customer-facing operations.
In Europe, Middle East & Africa, regulatory frameworks and multilingual complexity lead to conservative but strategic adoption patterns, with organizations prioritizing data sovereignty, privacy safeguards, and strong localization support. Firms in this region frequently opt for hybrid architectures that align with national data residency requirements and multilingual model adaptations. In Asia-Pacific, high mobile penetration and a diversity of languages and dialects accelerate innovation in on-device inference and low-cost hardware integration, particularly in consumer electronics and telecommunication scenarios. Regional partnerships and localized model training are common approaches to solving language and dialect coverage at scale. Collectively, these geographic distinctions inform go-to-market models, partnership decisions, and the prioritization of technical capabilities for vendors and integrators.
How platform providers, semiconductor innovators, and vertical specialists are aligning product, partnerships, and commercial models to capture differentiated value in speech solutions
Leading companies in the automatic voice and speech recognition ecosystem are executing divergent strategies to capture value across platforms, silicon, and services. Some vendors focus on full-stack solutions that integrate cloud-based training pipelines, managed inference services, and enterprise-grade security, targeting customers who prioritize rapid deployment and ongoing model improvements. By contrast, semiconductor and hardware suppliers emphasize acceleration and power-efficiency to enable high-performance on-device inference for automotive and embedded devices, partnering closely with software vendors to co-optimize stacks.
A third cohort of companies pursues specialization: offering best-in-class vertical solutions for healthcare transcription, legal compliance, or voice biometrics, often combining curated language models with domain-specific lexicons and annotation pipelines. Across these archetypes, successful players invest in robust developer tooling, open APIs, and transparent performance benchmarking to lower integration friction. Strategic alliances and selective acquisition activity tend to focus on filling capability gaps-particularly in multilingual modeling, noise robustness, and real-time speaker diarization-while pricing models evolve toward outcome-based contracting and consumption-aligned licensing to better match customer ROI expectations.
Practical recommendations for balancing edge performance, data governance, vendor flexibility, and ecosystem partnerships to accelerate reliable voice technology adoption
Industry leaders should adopt a pragmatic, multi-pronged approach that balances near-term delivery with long-term resilience to capture the strategic upside of voice technologies. First, they should prioritize hybrid deployment architectures that allow critical processing to occur at the edge where latency and privacy matter, while retaining centralized capabilities for continuous learning and analytics. Second, investment in data governance and annotation pipelines is essential to ensure domain accuracy and to prevent model drift; organizations should design feedback loops that incorporate human-in-the-loop validation for high-stakes contexts.
Next, procurement teams must reframe vendor selection to include flexibility for hardware substitution and software portability, negotiating contracts that allow modular upgrades and clear service-level commitments. Additionally, firms should develop competency in model evaluation metrics beyond raw accuracy, incorporating robustness to background noise, speaker variability, and adversarial inputs. Finally, leaders should pursue ecosystem partnerships strategically-partnering with semiconductor suppliers, systems integrators, and domain specialists to co-develop reference implementations and to accelerate time-to-value while sharing risk and IP.
A multi-method research approach combining practitioner interviews, technical assessment, and scenario-based validation to ensure robust and actionable insights
This research synthesizes insights from a multi-method approach combining primary engagement with industry practitioners and secondary analysis of technical literature, standards, and publicly available corporate disclosures. Primary methods included structured interviews with product leaders, system architects, procurement executives, and regulatory experts to capture lived implementation challenges and vendor selection criteria. These interviews were complemented by technical assessments of model architectures, inference frameworks, and edge computing platforms to understand trade-offs in latency, accuracy, and resource consumption.
Secondary analysis involved systematic review of academic and industry publications on recent model innovations, privacy-preserving techniques, and hardware accelerators, followed by triangulation of findings across sources. Data quality was enhanced through cross-validation with deployment case studies and vendor technical documentation. Finally, analytic conclusions were stress-tested using scenario analysis that considered variations in regulatory regimes, supply chain disruptions, and technology maturation timelines to ensure recommendations remain robust under different plausible futures.
A strategic synthesis emphasizing hybrid architectures, rigorous governance, and iterative deployments to maximize business value from voice and speech technologies
In conclusion, automatic voice and speech recognition technologies have entered a phase where technical maturity meets practical deployment complexity, requiring leaders to make deliberate choices about architecture, sourcing, and governance. The pace of model innovation enables richer, more natural interactions and broader language coverage, but it also imposes new responsibilities around privacy, fairness, and resiliency. Organizations that adopt hybrid architectures, invest in rigorous annotation and evaluation pipelines, and cultivate flexible supplier relationships will be positioned to extract disproportionate value from voice-enabled experiences.
Looking ahead, success will depend on the ability to operationalize continuous learning while maintaining transparent controls and user-centric privacy safeguards. By aligning technical investments with sector-specific requirements and by adopting an iterative deployment approach that learns from early pilots, enterprises can reduce risk and accelerate measurable business outcomes. These strategic imperatives should guide executive sponsorship, budgeting, and cross-functional coordination as voice technologies become increasingly integral to customer experiences and operational workflows.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
193 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of on-device edge processing for speech recognition to reduce latency and enhance privacy
- 5.2. Adoption of advanced deep learning transformer architectures to improve multilingual speech accuracy
- 5.3. Use of federated learning frameworks to personalize voice models while protecting user data privacy
- 5.4. Implementation of end-to-end neural speech recognition pipelines for lower error rates in noisy environments
- 5.5. Growing demand for real-time voice biometrics for secure authentication in financial and healthcare industries
- 5.6. Expansion of voice assistants with emotional and sentiment analysis for more natural user interactions
- 5.7. Emergence of voice-driven analytics platforms for call center performance and customer sentiment insights
- 5.8. Development of low-resource language models to support speech recognition in underrepresented dialects
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Automatic Voice & Speech Recognition Software Market, by Technology
- 8.1. Machine Learning-based Recognition
- 8.2. Deep Learning-based Recognition
- 8.3. Natural Language Processing (NLP)
- 8.4. Hybrid Systems
- 9. Automatic Voice & Speech Recognition Software Market, by Deployment Type
- 9.1. Cloud-Based
- 9.2. On-Premise
- 10. Automatic Voice & Speech Recognition Software Market, by Function
- 10.1. Speech Recognition
- 10.2. Voice Recognition
- 10.3. Voice Command Processing
- 10.4. Voice Analytics
- 11. Automatic Voice & Speech Recognition Software Market, by Application
- 11.1. Voice Commands
- 11.2. Transcription
- 11.3. Voice Analytics
- 11.4. Virtual Assistants
- 12. Automatic Voice & Speech Recognition Software Market, by End-User Industry
- 12.1. Healthcare
- 12.2. Automotive
- 12.3. Consumer Electronics
- 12.4. BFSI
- 12.5. Retail & E-commerce
- 13. Automatic Voice & Speech Recognition Software Market, by Region
- 13.1. Americas
- 13.1.1. North America
- 13.1.2. Latin America
- 13.2. Europe, Middle East & Africa
- 13.2.1. Europe
- 13.2.2. Middle East
- 13.2.3. Africa
- 13.3. Asia-Pacific
- 14. Automatic Voice & Speech Recognition Software Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Automatic Voice & Speech Recognition Software Market, by Country
- 15.1. United States
- 15.2. Canada
- 15.3. Mexico
- 15.4. Brazil
- 15.5. United Kingdom
- 15.6. Germany
- 15.7. France
- 15.8. Russia
- 15.9. Italy
- 15.10. Spain
- 15.11. China
- 15.12. India
- 15.13. Japan
- 15.14. Australia
- 15.15. South Korea
- 16. Competitive Landscape
- 16.1. Market Share Analysis, 2024
- 16.2. FPNV Positioning Matrix, 2024
- 16.3. Competitive Analysis
- 16.3.1. Nuance Communications, Inc.
- 16.3.2. Microsoft Corporation
- 16.3.3. Google LLC
- 16.3.4. Apple Inc.
- 16.3.5. Amazon.com, Inc.
- 16.3.6. International Business Machines Corporation
- 16.3.7. Sensory, Inc.
- 16.3.8. ReadSpeaker Holding B.V.
- 16.3.9. LumenVox LLC
- 16.3.10. OpenAI, L.L.C.
- 16.3.11. Verint Systems Inc.
- 16.3.12. VoiceVault Inc.
- 16.3.13. VoiceBase, Inc.
- 16.3.14. Speechmatics Ltd.
- 16.3.15. Acapela Group SA
- 16.3.16. Cerence Inc.
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