{"id":60268,"date":"2026-05-15T16:57:55","date_gmt":"2026-05-15T16:57:55","guid":{"rendered":"https:\/\/dmi5324.dotlogicstest.com\/?post_type=white-paper&#038;p=60268"},"modified":"2026-06-02T08:48:30","modified_gmt":"2026-06-02T08:48:30","slug":"from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai","status":"publish","type":"white-paper","link":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/","title":{"rendered":"From Speech Recognition to Speech Reasoning"},"content":{"rendered":"<h2><strong>Executive Summary<\/strong><\/h2>\n<p><strong>Building the Data Foundations for the Next Generation of Voice AI<\/strong><\/p>\n<p>Voice is rapidly becoming one of the most important interfaces for artificial intelligence (AI). Speech-enabled systems are now embedded across industries from customer support automation to meeting summarization, multilingual assistants, and conversational agents.<\/p>\n<p>For model builders, it is no longer sufficient for AI systems to simply transcribe what was said. Increasingly, organizations expect systems to interpret intent, understand nuance, and take action to deliver performance expected by end users. This evolution marks a fundamental transition -&gt; from speech recognition to speech reasoning.<\/p>\n<p>While advances in model architecture have driven significant improvements, a core bottleneck remains: data. Traditional speech datasets were designed for transcription accuracy, not contextual understanding. They lack structure, cultural nuance, and multilingual diversity. This results in systems that perform well in controlled environments but can struggle with commercial applications.<\/p>\n<p>This DATAmundi white paper explores this shift and outlines how organizations can build the data foundations and workflows required to enable real world speech AI systems underpinned with quality training data.<\/p>\n<h2><strong>From Recognition to Reasoning<\/strong><\/h2>\n<p>Automatic Speech Recognition (ASR), the task of converting spoken language into text, is a cornerstone of modern human-computer interaction, powering applications from voice assistants and dictation software to in-car control systems and automated transcription services.<\/p>\n<p>Over the past decade, ASR has undergone a profound transformation, evolving from hybrid statistical models such as GMM-HMM and DNN-HMM systems to end-to-end neural architectures and, more recently, Transformer and Conformer-based models capable of capturing long-range dependencies in speech. In parallel, self-supervised foundation models such as wav2vec 2.0 and large-scale weakly supervised models such as Whisper have substantially reduced reliance on transcribed data and improved robustness across accents, noise, and languages. This evolution has led to significant gains in transcription accuracy and efficiency, establishing ASR as a mature and well defined domain with standardized benchmarks, datasets, and evaluation metrics such as Word Error Rate (WER). <i>Source:<\/i> <a href=\"https:\/\/arxiv.org\/abs\/2510.12827\" target=\"_blank\" rel=\"noopener\"><i>Automatic Speech Recognition in the Modern Era: Architectures, Training, and Evaluation, Oct 2025<\/i><\/a><\/p>\n<p>However, this progress has also revealed a structural limitation: these systems are optimized to answer <i>what was said<\/i>, not <i>what it means<\/i>. Transcription is only the starting point for understanding true human communication.<\/p>\n<p>Consider a customer support scenario. A user says, \u201cI\u2019ve already called twice about this.\u201d A transcription system captures the words \u2013 in whatever language the system is working in. A reasoning system interprets frustration, recognizes repetition, and escalates accordingly. The difference is not linguistic accuracy but understanding.<\/p>\n<p>Speech reasoning systems \u2013 an emerging research direction within the broader field of Large Audio-Language Models (LALMs) and Speech LLMs, with dedicated benchmarks now appearing (e.g., <a href=\"https:\/\/arxiv.org\/abs\/2508.02018\" target=\"_blank\" rel=\"noopener\"><i>SpeechR (Yang et al., 2025)<\/i><\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2506.04779\" target=\"_blank\" rel=\"noopener\"><i>MMSU (Wang et al., 2025)<\/i><\/a>) \u2013 must interpret context, track conversational flow, and respond appropriately. They must handle interruptions, ambiguity, and real-world variability. This requires fundamentally different data.<\/p>\n<p><strong>Real World Use Cases<\/strong><\/p>\n<p>The impact of high quality speech data is evident across multiple use cases. Real world ASR performance depends not just on transcription accuracy, but on understanding intent, structure, and linguistic diversity across real human interactions<\/p>\n<ul>\n<li style=\"font-weight: 300;\" aria-level=\"1\">In customer support automation, systems trained on enriched datasets can detect frustration, urgency, and intent. This enables better routing, faster resolution, and improved customer experience.<\/li>\n<li style=\"font-weight: 300;\" aria-level=\"1\">In meeting summarization, structured data allows systems to identify key decisions, action items, and speaker contributions. This transforms raw transcripts into actionable insights.<\/li>\n<li style=\"font-weight: 300;\" aria-level=\"1\">In multilingual conversational agents, diverse datasets enable systems to handle accents, dialects, and mixed language interactions. This is critical for global deployments.<\/li>\n<\/ul>\n<h2><strong>The Real Bottleneck: Data, Not Models<\/strong><\/h2>\n<p>Whilst it\u2019s important to invest in development of the model itself, a priority success factor for AI teams is whether the data is fit for purpose and good enough to deliver desired results. The limiting factor being access to high quality, human validated, ethical data. Even if you have access to raw data, you need to convert it into reliable ASR training data, fit for scale.<\/p>\n<p>AI speech systems need data that captures not just words but meaning too.<\/p>\n<p><strong>Speech is Messy<\/strong><\/p>\n<p>Traditional ASR pipelines have been built around benchmark datasets such as LibriSpeech and Switchboard, which &#8211; while foundational &#8211; do not fully capture the diversity and unpredictability of real-world speech. Because in production environments, speech is messy. Conversations include overlapping speakers, interruptions, background noise, and shifting context. Speakers may use regional accents, switch between languages, or rely on tone and pacing to convey meaning.<\/p>\n<p>Speech is not just a technical signal, but a highly complex human behavior shaped by culture and many other factors. Meaning is influenced by tone, pacing, and interaction patterns.<\/p>\n<p>For example, overlapping speech may indicate engagement in one culture and interruption in another. A pause may signal hesitation, emphasis, or politeness. Even identical phrases can carry different meanings depending on delivery. When two people talk at once, AI transcription remains under pressure. Modern diarization and speaker-separation systems (for example pyannote, target-speaker ASR, and end-to-end neural diarization) have improved significantly, but well documented issues persist: error propagation in cascaded diarization-then-ASR pipelines, degraded accuracy in fully overlapped regions, the speaker permutation problem when the number of speakers is unknown, and weaker performance under low signal-to-noise conditions and far-field microphones. As a result, overlapping segments still typically need to be handled by human annotators who separate voices and tag the overlap correctly at the annotation stage.<\/p>\n<p>Traditional voice and audio datasets are typically clean and controlled. They feature single speakers, minimal noise, and standardized accents. While useful for benchmarking, they do not reflect live, real world conditions which is what model builders need to get the performance they want.<\/p>\n<p><strong>ASR Optimization vs Real World Needs<\/strong><\/p>\n<p>Many ASR systems are trained and evaluated using well established datasets and metrics, with WER serving as the dominant benchmark. While this has enabled rapid progress, it can narrow the objective of speech systems to transcription accuracy.<\/p>\n<p>A system may achieve a low WER score while still failing to:<\/p>\n<ul>\n<li style=\"font-weight: 300;\" aria-level=\"1\">Interpret user intent<\/li>\n<li style=\"font-weight: 300;\" aria-level=\"1\">Handle conversational context<\/li>\n<li style=\"font-weight: 300;\" aria-level=\"1\">Respond appropriately to tone or ambiguity<\/li>\n<\/ul>\n<p>This creates a fundamental mismatch between how systems are evaluated and how they are used.<\/p>\n<h2><strong>Domain-Specific AI Requires Domain-Specific Human Validation<\/strong><\/h2>\n<p>AI adoption is moving from broad experimentation to production use, and the market is shifting toward more task-specific models built for regulated and potentially high-risk sectors. <a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2025-04-09-gartner-predicts-by-2027-organizations-will-use-small-task-specific-ai-models-three-times-more-than-general-purpose-large-language-models\" target=\"_blank\" rel=\"noopener\">Gartner predicts<\/a> that by 2027, organizations will use small, task-specific AI models at least three times more than general-purpose LLMs.<\/p>\n<p>In speech and ASR, that means building systems designed not just to \u201chear words,\u201d but to understand specialist vocabulary, urgency, and context in fields such as healthcare, finance, and legal services is imperative. A medical triage AI assistant, for example, must be trained to distinguish between similar-sounding drug names and preserve urgency signals in a patient\u2019s voice (this intonation can vary across languages.) That is why providers such as Google offer dedicated medical speech models specifically for clinical conversations and dictation, rather than relying on general purpose ASR alone. Healthcare researchers are also increasingly leaning to voice AI as a major opportunity for patient communication and clinical workflows, but only when it is grounded in domain specific, context aware data.<\/p>\n<p>For training purposes, this is where transcription alone is not enough and where expert human validation becomes essential. For domain specific models, high-quality transcription cannot be treated as a generic language task. It requires two distinct layers of expertise working together:<\/p>\n<p>Domain experts validate whether the output is substantively correct for the use case: whether a clinical term, legal clause, or financial concept has been captured accurately, whether a risk signal has been missed, and whether the output is safe to use downstream.<\/p>\n<p>Language experts validate the linguistic layer: accents, dialects, code-switching, speaker overlap, punctuation, locale conventions, and cultural nuance.<\/p>\n<p>In practice, the gold standard is a workflow in which transcription and annotation are created and reviewed by trained linguists, then validated or adjudicated by subject-matter experts where the model will operate in specialist or regulated environments. That combination produces the kind of training and evaluation data that domain specific systems actually need.<\/p>\n<p>For AI leaders, the implication is straightforward: if the future is more domain-specific, then the data strategy must be expert-led with a human layer in the training and validation process.<\/p>\n<h2><strong>Rethinking Speech Data Workflows for Reasoning Systems<\/strong><\/h2>\n<p>To support reasoning and a rise in demand for domain specific datasets, speech data must evolve beyond transcription into structured, context rich datasets. This includes capturing speaker roles, timing, conversational dynamics, and acoustic signals such as laughter or overlap. These elements provide critical context that influences meaning. Consistency is equally important across large scale projects, especially those in multiple languages. Data annotation standards and guidelines must be clearly defined and applied uniformly across languages.<\/p>\n<p>[<i>Human-in-the-loop validation plays a critical role in maintaining quality and injecting reasoning into datasets to boost quality and performance from the lab to the commercial world. While AI tooling can accelerate and do the transcription, human expertise is required to interpret human nuance and ensure accuracy<\/i>.]<\/p>\n<h2><strong>The Infrastructure and Human Resources Gap<\/strong><\/h2>\n<p>Most model builders and enterprises lack the infrastructure and resources required to produce high quality speech data at scale. Workflows are often fragmented across multiple tools. Transcription, annotation, and quality assurance are handled separately, creating inefficiencies and inconsistencies.<\/p>\n<p>Scaling these workflows across languages introduces additional complexity. Managing linguists across multiple regions, maintaining consistent annotation guidelines, and ensuring outputs align across languages, dialects, and cultural contexts requires a level of coordination that many in-house teams are not equipped to handle.<\/p>\n<p>To close this gap, organizations need robust, automated workflows that bring structure and control to the process. This means creating a consistent, secure production environment where AI-driven tooling supports task allocation, quality checks, and workflow orchestration, while human experts provide validation, contextual understanding, and final judgment. By combining automation with human oversight, teams can scale efficiently without compromising on quality, ensuring outputs remain accurate, consistent, and fit for real-world AI speech applications.<\/p>\n<p>Human-in-the-loop validation after transcription is particularly valuable when training ASR systems for domain specific use cases or low resource languages, where higher variability and limited baseline data require more precise, context aware correction to achieve reliable accuracy.<\/p>\n<h2><strong>Real World Applications<\/strong><\/h2>\n<p>The impact of high quality speech data is evident across multiple use cases. Real world ASR performance depends not just on transcription accuracy, but on understanding intent, structure, and linguistic diversity across real human interactions<\/p>\n<p>In customer support automation, systems trained on enriched datasets can detect frustration, urgency, and intent. This enables better routing, faster resolution, and improved customer experience.<\/p>\n<p>In meeting summarization, structured data allows systems to identify key decisions, action items, and speaker contributions. This transforms raw transcripts into actionable insights.<\/p>\n<p>In multilingual conversational agents, diverse datasets enable systems to handle accents, dialects, and mixed language interactions. This is critical for global deployments.<\/p>\n<p><strong>CASE STUDY: <a href=\"https:\/\/dmi5324.dotlogicstest.com\/case-study\/multilingual-audio-transcription\/\">Scaling Multilingual Speech Data for Real World ASR<\/a><\/strong><\/p>\n<p><strong>Client:<\/strong> A leading AI-driven technology platform needed to convert large volumes of real world audio into reliable training data for ASR systems.<\/p>\n<p><strong>Challenge:<\/strong> Building a data pipeline to manage tens of thousands of short audio files, spanning 14 languages, with background noise, overlapping speakers, and inconsistent audio quality.<\/p>\n<p><strong>Solution<\/strong><strong>:<\/strong> The DATAmundi team delivered a scalable, human-in-the-loop solution combining AI-powered transcription, with expert linguistic human validation. Audio was processed through automated workflows and then refined and annotated by trained linguists, ensuring accuracy across languages and acoustic conditions.<\/p>\n<p><strong>Result:<\/strong> A production ready dataset delivered at scale and continuously ramping up the data pipeline from hundreds to over 20,000 files per week while maintaining over 90% quality pass rates and meeting strict performance thresholds.<\/p>\n<p>This enabled the client to improve ASR model performance in the real-world. This demonstrated that high quality, multilingual, and human validated data is critical to moving from speech recognition to true speech understanding.<\/p>\n<h2><strong>Practical Considerations for Building High-Quality Speech AI Data Pipelines<\/strong><\/h2>\n<p>As Speech AI evolves beyond transcription toward contextual understanding, the quality and structure of data pipelines become increasingly critical. Building robust Speech AI systems requires more than simply collecting audio. It demands scalable, governed, and human centric data operations throughout the model lifecycle.<\/p>\n<p><strong>Start with Real World, Representative Data<\/strong><\/p>\n<p>Prioritize naturally occurring speech in custom data collection or validated <a href=\"https:\/\/dmi5324.dotlogicstest.com\/ai-data-services\/off-the-shelf-ai-training-datasets\/\">off-the-shelf datasets<\/a> with clear governance, compliance, and traceability. For global applications, datasets should reflect a broad range of languages, accents, dialects, speaking styles, environments, and acoustic conditions.<\/p>\n<p><strong>Use Secure and Scalable Transcription Workflows<\/strong><\/p>\n<p>Whether using AI-powered transcription and\/or human annotators, workflows should be integrated, secure, and operationally scalable. Human transcription and annotation should involve native speakers, trained linguists, and domain experts when quality and nuance are critical.<\/p>\n<p><strong>Implement Multi-Pass Quality Assurance<\/strong><\/p>\n<p>Single pass labelling is not always sufficient for high performing Speech AI systems. Multi-pass QA review, including double or triple annotation, benchmark datasets, and continuous evaluation processes, helps improve consistency and reduce error rates.<\/p>\n<p><strong>Ensure Diverse and Balanced Coverage<\/strong><\/p>\n<p>High quality datasets require balanced representation across languages, accents, genders, demographics, and industry domains to support model robustness and reduce bias.<\/p>\n<p><strong>Incorporate Structured and Contextual Labelling<\/strong><\/p>\n<p>Speech understanding increasingly depends on context, not just words. Beyond transcription, pipelines should capture intent, sentiment, conversational flow, speaker turns, and other structural signals that improve downstream model performance.<\/p>\n<p><strong>Maintain Human-in-the-Loop Evaluation<\/strong><\/p>\n<p>Human oversight remains essential for validating outputs, identifying edge cases, refining annotation quality, and continuously improving model behaviour over time.<\/p>\n<p><strong>Establish Strong Governance and Traceability<\/strong><\/p>\n<p>Speech AI pipelines should include clear sourcing documentation, annotation guidelines, bias monitoring, compliance controls, and end-to-end traceability to support responsible and auditable AI development.<\/p>\n<h2><strong>The Future of Voice AI: From Transcribing Speech to Understanding People<\/strong><\/h2>\n<p>The next generation of voice AI must interpret intent, understand context, and respond appropriately in real world environments shaped by ambiguity, culture, and human nuance.<\/p>\n<p>As AI model builders and enterprises scale voice AI systems across markets, languages, and domains, the gap between model capability and real world performance will increasingly be defined by data. Models will continue to improve, but without the right speech data foundations \u2013 context rich, multilingual, and human validated &#8211; systems will struggle to move beyond surface level understanding.<\/p>\n<p>The future of voice AI will be built on structured, enriched speech datasets, designed not just for accuracy but for meaning.<\/p>\n<p>For AI leaders, the implication is clear. Building high performing speech systems is no longer just a modeling challenge. It is a data challenge. Those who invest in high quality, domain specific, and multilingual data pipelines will be best positioned to unlock the full potential of voice AI.<\/p>\n<p><strong>Powering the Next Generation of Voice AI with DATAmundi<\/strong><\/p>\n<p>At DATAmundi, we specialize in building the human data foundations required to move from speech recognition to speech reasoning. Our <a href=\"https:\/\/datatalent.ai\/\" target=\"_blank\" rel=\"noopener\">DATAtalent<\/a> global network of linguists, annotators, and subject-matter experts enables the creation of high quality, multilingual datasets designed for real world AI performance in 200+ languages, including rare and low-resource languages.<\/p>\n<p>With AIDA Speech, our data infrastructure layer for speech AI, we provide end-to-end solutions for Voice AI including custom data collection in multiple languages, AI-driven transcription tooling and workflow orchestration with expert human validation, all delivered through our <a href=\"https:\/\/dmi5324.dotlogicstest.com\/ai-data-services\/aida-hub\/\">AIDA Hub<\/a> data operations platform.<\/p>\n<p>AIDA Speech transcription generates structured draft transcriptions from multilingual, multi-speaker, and acoustically complex inputs. Unlike generic transcription engines, AIDA Speech is built specifically for AI data pipelines, producing outputs that are ready for downstream annotation, QA validation, and model training. While many speech-to-text solutions focus purely on automated transcription, AIDA Speech is designed as a human-in-the-loop system that bridges AI and real world linguistic complexity to deliver production ready speech data.<\/p>\n<p>Whether accelerating early model development with off-the-shelf datasets or delivering custom, context rich speech data pipelines for complex use cases, we help AI teams build the next generation of voice AI.<\/p>\n<p><code><\/code><\/p>\n<div class=\"hs-cta-embed hs-cta-simple-placeholder hs-cta-embed-414218650856\" style=\"max-width: 100%; max-height: 100%; width: 502px; height: 100.3888931274414px;\" data-hubspot-wrapper-cta-id=\"414218650856\"><a href=\"https:\/\/cta-eu1.hubspot.com\/web-interactives\/public\/v1\/track\/redirect?encryptedPayload=AVxigLJj%2F6DfnbOaRfkR%2Bbk5eMNRMyVuk7EJ6wePQq4xBp2KbaPJqUfC79Rs98jmCVP3vQNJ7C%2FTZl5QWZebYLeiKzarFor7rAcZszQkV0gxajmfcMumly9LrPEEF0RPg3fhP1IRV209S4Id1IHOj0bbbh%2FODZV3gfr5sNVhW0zrLH%2FS&amp;webInteractiveContentId=414218650856&amp;portalId=45499902\" target=\"_blank\" rel=\"noopener\"><br \/>\n<img decoding=\"async\" style=\"height: 100%; width: 100%; object-fit: fill;\" src=\"https:\/\/hubspot-no-cache-eu1-prod.s3.amazonaws.com\/cta\/default\/45499902\/interactive-414218650856.png\" alt=\"Let's talk about your next project.\u00a0\" \/><br \/>\n<\/a><\/div>\n<p><code><\/code><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Explore DATAmundi\u2019s white paper on building Speech AI data pipelines from transcription to reasoning, and why context-rich, multilingual, human-validated data is critical for next-generation Voice AI systems.<\/p>\n","protected":false},"featured_media":60676,"template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"white-papers":[119],"class_list":["post-60268","white-paper","type-white-paper","status-publish","has-post-thumbnail","hentry","white-papers-blog-style"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.3 (Yoast SEO v28.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>From Speech Recognition to Speech Reasoning | DATAmundi<\/title>\n<meta name=\"description\" content=\"Explore the future of Speech AI and how it transforms voice interfaces into smart systems that understand context and intent.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"From Speech Recognition to Speech Reasoning\" \/>\n<meta property=\"og:description\" content=\"Explore the future of Speech AI and how it transforms voice interfaces into smart systems that understand context and intent.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/\" \/>\n<meta property=\"og:site_name\" content=\"DATAmundi\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-02T08:48:30+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dmi5324.dotlogicstest.com\/wp-content\/uploads\/2026\/05\/Web-Graphics.avif\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Szacowany czas czytania\" \/>\n\t<meta name=\"twitter:data1\" content=\"12 minut\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-paper\\\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\\\/\",\"url\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-paper\\\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\\\/\",\"name\":\"From Speech Recognition to Speech Reasoning | DATAmundi\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-paper\\\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-paper\\\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Web-Graphics.avif\",\"datePublished\":\"2026-05-15T16:57:55+00:00\",\"dateModified\":\"2026-06-02T08:48:30+00:00\",\"description\":\"Explore the future of Speech AI and how it transforms voice interfaces into smart systems that understand context and intent.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-paper\\\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\\\/#breadcrumb\"},\"inLanguage\":\"pl-PL\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-paper\\\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pl-PL\",\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-paper\\\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\\\/#primaryimage\",\"url\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Web-Graphics.avif\",\"contentUrl\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/wp-content\\\/uploads\\\/2026\\\/05\\\/Web-Graphics.avif\",\"width\":1920,\"height\":1080},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-paper\\\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"White Papers\",\"item\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-papers\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Blog style\",\"item\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/white-papers\\\/blog-style\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"From Speech Recognition to Speech Reasoning\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/#website\",\"url\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/\",\"name\":\"DATAmundi\",\"description\":\"Our Data. Your AI.\",\"publisher\":{\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pl-PL\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/#organization\",\"name\":\"DATAMundi\",\"url\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pl-PL\",\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/wp-content\\\/uploads\\\/2025\\\/03\\\/dark_bg.svg\",\"contentUrl\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/wp-content\\\/uploads\\\/2025\\\/03\\\/dark_bg.svg\",\"width\":520,\"height\":55,\"caption\":\"DATAMundi\"},\"image\":{\"@id\":\"https:\\\/\\\/dmi5324.dotlogicstest.com\\\/pl\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"From Speech Recognition to Speech Reasoning | DATAmundi","description":"Explore the future of Speech AI and how it transforms voice interfaces into smart systems that understand context and intent.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/","og_locale":"pl_PL","og_type":"article","og_title":"From Speech Recognition to Speech Reasoning","og_description":"Explore the future of Speech AI and how it transforms voice interfaces into smart systems that understand context and intent.","og_url":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/","og_site_name":"DATAmundi","article_modified_time":"2026-06-02T08:48:30+00:00","og_image":[{"width":1920,"height":1080,"url":"https:\/\/dmi5324.dotlogicstest.com\/wp-content\/uploads\/2026\/05\/Web-Graphics.avif","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_misc":{"Szacowany czas czytania":"12 minut"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/","url":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/","name":"From Speech Recognition to Speech Reasoning | DATAmundi","isPartOf":{"@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/#website"},"primaryImageOfPage":{"@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/#primaryimage"},"image":{"@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/#primaryimage"},"thumbnailUrl":"https:\/\/dmi5324.dotlogicstest.com\/wp-content\/uploads\/2026\/05\/Web-Graphics.avif","datePublished":"2026-05-15T16:57:55+00:00","dateModified":"2026-06-02T08:48:30+00:00","description":"Explore the future of Speech AI and how it transforms voice interfaces into smart systems that understand context and intent.","breadcrumb":{"@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/#breadcrumb"},"inLanguage":"pl-PL","potentialAction":[{"@type":"ReadAction","target":["https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/"]}]},{"@type":"ImageObject","inLanguage":"pl-PL","@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/#primaryimage","url":"https:\/\/dmi5324.dotlogicstest.com\/wp-content\/uploads\/2026\/05\/Web-Graphics.avif","contentUrl":"https:\/\/dmi5324.dotlogicstest.com\/wp-content\/uploads\/2026\/05\/Web-Graphics.avif","width":1920,"height":1080},{"@type":"BreadcrumbList","@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-paper\/from-speech-recognition-to-speech-reasoning-building-the-data-foundations-for-the-next-generation-of-voice-ai\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"White Papers","item":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-papers\/"},{"@type":"ListItem","position":2,"name":"Blog style","item":"https:\/\/dmi5324.dotlogicstest.com\/pl\/white-papers\/blog-style\/"},{"@type":"ListItem","position":3,"name":"From Speech Recognition to Speech Reasoning"}]},{"@type":"WebSite","@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/#website","url":"https:\/\/dmi5324.dotlogicstest.com\/pl\/","name":"DATAmundi","description":"Our Data. Your AI.","publisher":{"@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/dmi5324.dotlogicstest.com\/pl\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pl-PL"},{"@type":"Organization","@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/#organization","name":"DATAMundi","url":"https:\/\/dmi5324.dotlogicstest.com\/pl\/","logo":{"@type":"ImageObject","inLanguage":"pl-PL","@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/#\/schema\/logo\/image\/","url":"https:\/\/dmi5324.dotlogicstest.com\/wp-content\/uploads\/2025\/03\/dark_bg.svg","contentUrl":"https:\/\/dmi5324.dotlogicstest.com\/wp-content\/uploads\/2025\/03\/dark_bg.svg","width":520,"height":55,"caption":"DATAMundi"},"image":{"@id":"https:\/\/dmi5324.dotlogicstest.com\/pl\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/dmi5324.dotlogicstest.com\/pl\/wp-json\/wp\/v2\/white-paper\/60268","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dmi5324.dotlogicstest.com\/pl\/wp-json\/wp\/v2\/white-paper"}],"about":[{"href":"https:\/\/dmi5324.dotlogicstest.com\/pl\/wp-json\/wp\/v2\/types\/white-paper"}],"version-history":[{"count":0,"href":"https:\/\/dmi5324.dotlogicstest.com\/pl\/wp-json\/wp\/v2\/white-paper\/60268\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dmi5324.dotlogicstest.com\/pl\/wp-json\/wp\/v2\/media\/60676"}],"wp:attachment":[{"href":"https:\/\/dmi5324.dotlogicstest.com\/pl\/wp-json\/wp\/v2\/media?parent=60268"}],"wp:term":[{"taxonomy":"white-papers","embeddable":true,"href":"https:\/\/dmi5324.dotlogicstest.com\/pl\/wp-json\/wp\/v2\/white-papers?post=60268"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}