June 2026 Summaries

Key data extraction from call audio in contact centers is a multi-layered process that begins with transcription accuracy, which is crucial for downstream operations like Quality Assurance (QA) and Customer Relationship Management (CRM). Errors in the transcription layer, such as misinterpretations of names or numbers, can lead to significant operational inefficiencies and compliance risks, as these errors propagate through every subsequent system. The Solaria-1 model, benchmarked for lower Word Error Rate (WER) and Diarization Error Rate (DER), addresses these challenges by improving transcription accuracy, especially in environments with phonetic ambiguity and regional accents. Named Entity Recognition (NER) plays a pivotal role by identifying key data points like account numbers and customer intents, converting them into structured, machine-readable formats. The integration of these extracted entities into CRM and QA platforms through structured JSON outputs enhances data reliability and reduces manual corrections, ultimately lowering operational costs. Additionally, speaker diarization and custom NER schemas further refine data accuracy by ensuring correct entity attribution and accommodating industry-specific requirements, respectively. The document emphasizes the importance of ongoing monitoring and adaptation of transcription models to maintain high precision and recall rates, particularly in dynamic environments with language shifts and varying accent profiles.

Scaling global customer support efficiently necessitates more than just adding translation technology, with transcription accuracy being crucial for maintaining the integrity of CRM entries and QA processes. Offshore BPO staffing can significantly reduce costs, but this is only effective if the audio infrastructure can handle accents and code-switching without errors. The primary challenges include managing structural issues like regional labor costs, dialect variations, regulatory compliance, and translation latencies that affect Average Handle Time. A hybrid model combining AI and human agents, known as HumAIn, optimizes cost efficiency by delegating routine tasks to AI while reserving complex interactions for human judgment. Accurate transcription is vital for automated QA and coaching, as errors can propagate through systems, affecting data quality and compliance. Real-time AI translation can improve language coverage and reduce Average Handle Time without compromising customer satisfaction, but the system's success hinges on its ability to handle diverse dialects and maintain low latency. The balance between AI automation and human agents is critical, especially for high-value or compliance-sensitive interactions, requiring careful consideration of the interaction complexity, regulatory risks, and customer value to determine the appropriate staffing model.

Amazon Connect transcription services face challenges with high costs and accuracy issues, particularly for multilingual scenarios and long call durations. To address these, routing audio through Kinesis Video Streams or S3 to Solaria-1 is recommended, as it eliminates the risk of AWS Lambda's 15-minute timeout and reduces Word Error Rate (WER) by 29% compared to other solutions. The native transcription issues can lead to corrupted CRM records and compliance risks. Solaria-1 offers a more accurate and cost-effective solution, especially in environments with accented or multilingual speech, by integrating with existing Amazon Connect infrastructure without altering the telephony layer. This setup ensures enhanced real-time transcription and post-call analysis capabilities, providing structured outputs for CRM and QA platforms. The comprehensive solution includes features like sentiment analysis, PII redaction, and code-switching detection, which are critical for high-volume contact centers operating in diverse linguistic environments.

Custom vocabulary support in speech-to-text systems helps improve accuracy for domain-specific terms by adjusting the model's decoding behavior to recognize specific technical terms, brand names, or acronyms. This approach is particularly useful in contact centers, where generic models might misinterpret specialized terminology, leading to errors in CRM data, QA scorecards, and automated summaries. Runtime vocabulary lists are ideal for dynamic, frequently changing term sets, while model fine-tuning suits stable vocabularies in unique acoustic environments. It's crucial to manage vocabulary list length to avoid degrading accuracy by unnecessarily expanding the search space. Custom vocabulary should be updated in sync with product release cycles, and its impact measured using keyword error rate (KER) rather than global word error rate (WER) to ensure precise transcription of high-value keywords. Custom spelling complements this by performing string replacements for consistent misspellings. The integration of custom vocabulary requires careful configuration to optimize performance without adding significant latency or error rates, and it is often more cost-effective and efficient than model fine-tuning for dynamic environments.

The integration of a speech-to-text infrastructure with the Vonage Voice API streamlines fragmented processes into a single API, offering approximately 270ms real-time latency for live agent assistance and post-call batch processing for QA scoring. This integration allows contact centers to improve agent efficiency by providing real-time transcripts and automated QA scoring, reducing manual review workloads and potential errors in call logs, which could otherwise corrupt CRM data and compliance records. The setup involves routing Vonage WebSocket streams to transcription endpoints, enabling live and post-call analysis with varying latency preferences, while maintaining compliance with regulatory standards like PCI-DSS, HIPAA, and GDPR. The system supports multiple languages and offers features such as speaker diarization, sentiment analysis, and named entity recognition, which enhance QA accuracy and coaching effectiveness. Additionally, the API's flexibility accommodates multilingual and code-switching scenarios, crucial for BPO environments, and offers different plan tiers to manage data governance and usage-based expenses.

Voice analytics in call centers serve as an automated solution for capturing, transcribing, and analyzing phone conversations, which helps overcome the limitations of manual Quality Assurance (QA) processes that typically review only a small sample of calls. By converting raw phone calls into structured data, voice analytics provide insights into sentiment, compliance, talk time, and agent behavior patterns. The technology faces challenges due to narrowband telephony audio, which can degrade transcription accuracy and affect QA scorecards and CRM entries. A critical component of the voice analytics pipeline is the Speech-to-Text (STT) engine which determines the reliability of outputs. Real-time transcription aids live agent assistance, whereas post-call processing is ideal for QA scoring and compliance auditing. Voice analytics integrate into core business systems through structured data outputs, offering enhanced operational outcomes such as improved QA coverage, reduced Average Handle Time (AHT), and better coaching based on data insights. The technology supports multilingual environments and is crucial for maintaining accuracy across global dialects, ensuring effective compliance monitoring, and providing operational metrics like sentiment scores and script compliance.

Call center analytics play a crucial role in bridging the gap left by traditional manual QA processes, which typically review less than 2% of calls, leading to operational blind spots such as missed compliance and coaching opportunities. Effective analytics rely heavily on transcription accuracy, with metrics like Word Error Rate (WER) and Diarization Error Rate (DER) setting the foundation for reliable data that informs QA scorecards, CRM workflows, and coaching systems. The distinction between call center and contact center analytics is important, as the former focuses on voice interactions while the latter encompasses all customer channels, affecting how data is integrated and which KPIs are prioritized. Structured transcripts are key to transforming raw call audio into actionable data, supporting a consistent analytics framework across multilingual operations. Accurate transcription ensures that errors do not propagate through systems, improving metrics such as First Call Resolution (FCR), Customer Satisfaction (CSAT), and Average Handle Time (AHT), while also optimizing cost-per-contact and agent coaching outcomes. The guide emphasizes the necessity of selecting a reliable transcription vendor and integrating real-time metrics into dashboards to enhance decision-making and operational efficiency, ultimately supporting a data-driven approach in modern contact centers.

Call center automation, driven by AI advancements, significantly reduces operational costs while enhancing efficiency, yet its success is heavily reliant on the accuracy of the transcription layer. Misinterpretations in speech-to-text systems can lead to errors that silently affect downstream processes such as CRM logging, quality assurance, and coaching scorecards, making precise transcription critical. Modern AI systems have evolved to handle complex workflows and routing decisions autonomously, improving service quality and consistency across channels. Automation touches all stages of the call lifecycle, from pre-call routing to post-call QA, with each phase benefiting from reliable structured data. Real-time transcription aids agents by providing contextual prompts, potentially reducing Average Handle Time (AHT) and improving First Call Resolution (FCR), though inaccuracies can lead to unresolved issues and increased repeat contacts. Effective call center AI deployment requires careful planning, including handling accented speech and ensuring compliance with data protection standards, to avoid common pitfalls and maximize ROI.

In the context of contact center operations, the challenge of accurately extracting named entities from call transcripts is highlighted, particularly due to the limitations of standard Named Entity Recognition (NER) models when applied to Automated Speech Recognition (ASR) outputs. Such models, trained primarily on clean text, often suffer significant performance drops, leading to missed or corrupted data entries in Customer Relationship Management (CRM) systems. This issue is compounded by transcription errors, disfluencies, and accent-driven phonetic variations. The proposed solution involves improving the transcript quality at the ASR layer using advanced models like Solaria-1, which reduces Word Error Rate (WER) and Disfluency Error Rate (DER), thus providing a more reliable text foundation for NER processes. The text also emphasizes the need for precise entity extraction, especially in regulated industries, to avoid operational risks associated with false positives and negatives. A robust NER pipeline, incorporating Named Entity Disambiguation (NED) and Named Entity Linking (NEL), is essential for transforming raw conversational audio into structured CRM data, while maintaining data integrity and compliance.

Ani Ghazaryan's guide, published on June 19, 2026, explores the complexities of reducing after-call work (ACW) through automated transcription technologies, highlighting the choice between packaged conversation intelligence (CI) platforms and speech-to-text (STT) APIs. The guide explains that the decision depends on factors such as call volume, language requirements, and workflow ownership needs. While CI platforms offer fast deployment and bundled features, they can be rigid and costly in terms of per-seat pricing. In contrast, STT APIs provide flexible, usage-based pricing and better multilingual support but require more engineering effort. The discussion emphasizes that ACW is not just about transcription but involves workflow orchestration and CRM synchronization. It also covers the hidden engineering and operational costs of building in-house solutions versus the potential constraints of vendor-controlled platforms. The article examines various industry players and their offerings, such as Gladia, AssemblyAI, and Deepgram, and provides insights into pricing models, integration complexities, and the advantages of structured data outputs in automating post-call processes.

In the context of contact centers, the decision between real-time and asynchronous transcription hinges on architectural fit rather than latency, with each mode serving distinct workflows. Asynchronous transcription, processed via REST API calls, is optimal for post-call tasks such as QA scoring, CRM enrichment, and compliance archiving due to its lower Word Error Rates (WER) and cost efficiency. Real-time transcription, utilizing WebSocket streaming, is reserved for live-call scenarios where sub-300ms latency is crucial, such as live agent assist and IVR routing. Many Contact Center as a Service (CCaaS) platforms mistakenly default to real-time transcription, incurring higher costs and reduced accuracy for post-call analytics. Asynchronous batch processing provides a more cost-effective and accurate solution for most contact center tasks, while real-time transcription remains essential for immediate, live interactions. The integration of both transcription modes through a single platform allows contact centers to choose based on workflow needs without switching vendors, thereby optimizing both costs and transcription accuracy.

The text explores the complexities and considerations involved in selecting between AI call analytics platforms and Speech-to-Text (STT) APIs for multilingual transcription at scale, focusing on factors such as language coverage, code-switching, accent handling, and pricing structures. It highlights the challenges of relying solely on English benchmarks when scaling to multilingual markets and the importance of testing models on specific languages and accents relevant to a company's operations. Gladia's Solaria models are emphasized for their breadth and depth in language support, with Solaria-1 covering over 100 languages with real-time code-switching capabilities, making it suitable for diverse global markets. The text also discusses the economic implications of different pricing models, where some platforms charge separately for features like diarization and sentiment analysis, while Gladia's plans bundle these at a base rate. It underscores the importance of evaluating WER (Word Error Rate) in real-world conditions and ensuring accuracy for diverse accents and specific industry jargon, recommending teams to conduct tests with their own production recordings to make informed decisions.

Word Error Rate (WER) is a critical metric in conversation intelligence, impacting the accuracy of downstream features such as sentiment analysis, CRM enrichment, and compliance monitoring. A 5% WER on a typical five-minute call can result in approximately 38 incorrect words, often affecting key terms like product names and compliance phrases. These transcription errors propagate through conversation intelligence systems, leading to issues like sentiment inversion, compliance gaps, and CRM mismatches. Real-time transcription, while providing low latency, often results in higher WER due to limited context, whereas asynchronous processing can improve accuracy by utilizing the full audio context. The effects of WER are compounded in multilingual and noisy audio environments, where language-specific tokenizer limitations further degrade accuracy. Addressing WER involves implementing custom vocabularies, consistent spelling, and fine-tuning models to improve transcription reliability, which in turn reduces errors in downstream applications and mitigates the risk of business and compliance failures.

Customer sentiment analysis in contact centers leverages advanced methods and tools to capture the emotional tone of interactions, providing insights beyond traditional CSAT surveys. High-accuracy transcription and speaker diarization are crucial for effective sentiment scoring, as they enable differentiation between customer and agent emotions across all interactions, rather than a sampled few. This comprehensive monitoring can reveal early signs of customer churn risk and agent burnout, allowing for timely interventions. Machine learning models, particularly transformer-based NLP models, outperform lexicon-based systems by analyzing the full conversational context, offering more nuanced sentiment assessments such as emotion detection and aspect-based analysis. Challenges arise with accented speech and code-switching, highlighting the importance of robust audio processing. Real-time sentiment insights can streamline QA processes and enhance workforce management by predicting stress periods and optimizing staffing. The integration of sentiment analysis with CRM systems aids in automating QA and improving customer retention strategies. Ensuring transcription accuracy is fundamental, as errors can propagate through subsequent analyses, affecting sentiment reliability and operational decisions.

Real-time speech-to-text (STT) for contact centers needs to balance latency and accuracy, with sub-300ms latency aligning with human conversational pauses, yet focusing solely on speed can lead to errors that degrade the product. The latency budget encompasses audio capture, STT inference, natural language understanding (NLU), and text-to-speech (TTS), with each step consuming a portion. Partial transcript stability is crucial as intermediate outputs influence IVR routing and agent assist, and frequent changes can cause misrouting and irrelevant prompts. While many teams prioritize speed, issues arise when transcripts are fast but inaccurate, impacting agent assist and customer satisfaction scores (CSAT). Real-time transcription differs from batch processing, as it streams partial outputs, which downstream systems use immediately. For effective real-time applications, the focus should be on achieving stable, actionable transcripts within the natural pause window. Models like Solaria-1, optimized for multilingual and noisy environments, offer approximately 270ms responsiveness, supporting over 100 languages, which is beneficial for global contact centers. Evaluating STT providers requires testing on authentic contact center audio, ensuring sub-300ms latency targets while considering additional costs and conducting a real-world pilot to measure performance under production conditions.

Audio-to-LLM is a feature by Gladia that streamlines the process of generating structured summaries and action items from meeting transcripts by combining transcription and language model (LLM) inference into a single API call. This innovation eliminates the need for separate transcription services and LLM pipelines, allowing users to transcribe audio and execute LLM prompts in one step. Users can define multiple prompts per request, enabling diverse outputs such as CRM updates, Slack digests, and Linear tickets from a single meeting recording. The output structure is entirely defined by the prompts, allowing customization for specific roles like sales representatives or developers, ensuring the generated summaries and action items are immediately useful without further formatting. With access to over 400 models, users can select the most appropriate one for each meeting type, optimizing for factors like cost and output quality without altering their pipeline architecture. This tool does not require domain-specific tuning, as adaptability is controlled through prompt design, which can be tested against real meeting recordings to ensure effectiveness across various scenarios.

Solaria-3 is Gladia's latest and most accurate speech-to-text model, specifically designed to excel in real-world, noisy, multi-speaker, and accented audio environments commonly found in European business contexts. It has achieved the highest accuracy in several benchmarks and internal tests, outperforming other major providers like AssemblyAI, ElevenLabs, and Deepgram on challenging audio conditions, including fast-paced calls and non-native accented English. While Solaria-3 is optimized for five European languages—English, French, German, Spanish, and Italian—and excels in production audio, Solaria-1 remains superior for clean, formal read-speech and offers broader multilingual coverage with support for over 100 languages. The two models are intended to complement each other rather than replace one another, allowing users to choose based on their specific audio needs. Solaria-3's compliance with various data protection standards and its availability on both EU and US clusters with full data sovereignty further enhance its appeal for enterprise use.

In the contact center industry, the choice of automation use cases can significantly impact both customer satisfaction and operational efficiency. The text emphasizes the importance of starting with post-call asynchronous workflows, such as automated transcription and CRM updates, which are low-risk and yield measurable returns quickly. Customer-facing automation, like chatbots, is high-risk due to potential visible failures, which can damage brand reputation. By using a four-factor framework (volume, repetition, accuracy threshold, and ROI timeline), companies can effectively identify automation opportunities that balance risk with economic benefit. The text also highlights the importance of selecting reliable STT (speech-to-text) providers and ensuring high transcription accuracy, especially in multilingual environments. The use of automation in post-call processes not only improves data quality but also provides foundational accuracy data, which is crucial before deploying customer-facing AI solutions.

The guide explores various integration methods for connecting call data to CRM and workflow tools, focusing on the importance of accurate transcription as the foundation for reliable downstream data. It details four integration paths—Zapier for rapid prototyping, Make.com for complex routing, n8n for high-volume privacy-sensitive workloads, and direct REST API for robust production environments. Highlighting Gladia's Solaria-1 model, which offers superior transcription accuracy with a 29% lower Word Error Rate (WER) and 3x lower Diarization Error Rate (DER) compared to alternatives, the document emphasizes that the integrity of CRM data hinges on the initial transcription quality. It explains the data flow process from audio capture through transcription and structured output to destination routing in CRM systems like HubSpot, Salesforce, Airtable, Slack, or Notion, and discusses the benefits of Gladia's native enrichment features such as sentiment analysis, named entity recognition, and summarization. The guide also provides insights into choosing the right automation tool based on team skills, processing volume, and the integration's role within a product, while offering practical advice for setting up and optimizing Gladia pipelines for various business needs.

AI transcription for support calls is generally legal, but compliance with consent laws varies regionally, with specific requirements in the US, EU, and UK. In the US, 13 states require all-party consent, while the EU mandates a legal basis under GDPR, and unconsented recording is a criminal offense in Germany. Transcription vendors like Gladia offer different data handling policies, where customer audio is not used for model training on higher-tier plans, and PII redaction must be explicitly configured. Compliance involves managing the entire lifecycle of audio data, from consent to storage and deletion, with particular attention to PII and PCI DSS requirements for handling sensitive information. Misconfigurations can lead to significant legal exposure, and thorough vendor evaluation, beyond accuracy benchmarks, is crucial for regulated environments. Data residency options and appropriate certifications, such as SOC 2 Type II and ISO 27001, are essential to ensure compliance with data protection standards like GDPR and CCPA.

The guide outlines the evolution of customer support call flows from traditional IVR systems to AI-augmented solutions, highlighting the importance of real-time transcription in improving call handling efficiency. Traditional systems often struggle with issues like language switches and high transcription latency, leading to errors in intent detection and routing. AI-augmented flows, however, utilize advanced speech-to-text technologies with sub-300ms latency to support live agent assistance and ensure accurate post-call summaries. These systems integrate steps like greeting, authentication, dynamic routing, and issue resolution while maintaining structured data flow throughout the process. The guide emphasizes the significance of managing transcription latency to enhance agent guidance in real-time and discusses the build-versus-buy considerations for implementing such AI architectures. Additionally, it explores how AI can optimize multilingual interactions and post-call data handling, thereby boosting first-call resolution rates and overall customer satisfaction.

Call transcription accuracy is crucial for contact centers, yet public Speech-to-Text (STT) benchmarks often fail to reflect real-world performance due to their reliance on clean, English audio, which contrasts sharply with the diverse and noisy environments of actual contact center calls. To properly evaluate STT vendors, it's essential to measure metrics such as Word Error Rate (WER) overall and per language and accent, Diarization Error Rate (DER), latency percentiles (p50, p95, p99), and code-switching accuracy using the contact center's own production audio. Self-reported accuracy claims are unreliable without transparent methodologies, and hidden costs for features like diarization and Named Entity Recognition (NER) can accumulate significantly. A successful evaluation requires a comprehensive testing methodology that includes diverse acoustic conditions, languages, speaker demographics, and difficulty tiers to accurately predict production performance and economic viability. The guide emphasizes the importance of establishing standardized transcription benchmarks tailored to the specific needs and conditions of contact centers to avoid flawed analytics and the propagation of errors through downstream systems.

Bruno Hays, a Lead ML Speech Engineer at Gladia, developed a novel approach to improve real-time multilingual automatic speech recognition (ASR) with code-switching by creating a lightweight, modular ensemble system that efficiently routes between small, specialized models instead of relying on a large multilingual model. This system, which is fully open source, uses a Voice Activity Detection (VAD) component to identify speech boundaries, Streaming Zipformer models for ASR, and a Language Identification (LID) system for detecting language switches. The Asynchronous Rollback Pipeline method reduces language lag by instantly transcribing audio with the active ASR engine, monitoring for language changes, and adjusting the transcription as needed. This approach outperforms larger models in inter-utterance code-switching scenarios, achieving a 13% Word Error Rate (WER), but struggles with intra-utterance switching, where it falls behind cloud APIs despite performing better than some local models. The results suggest that future ASR systems could benefit from using small, specialized models with intelligent routing, offering a more efficient solution for local, on-device multilingual ASR tasks.