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Migrating from Rev.ai to Gladia: what global teams should know
TL;DR: At 10,000 hours of audio per month, Rev.ai's per-hour billing compounds quickly once you add diarization, translation, and sentiment as separate line items. Language coverage gaps surface silently in production when non-English or accented audio degrades without returning an obvious error. This guide gives you the exact API payload mappings, WebSocket transition logic, and a TCO model at realistic scale to make a defensible evaluation of switching. If you decide to migrate, our all-inclusive per-hour pricing bundles every audio intelligence feature at the base rate, and most teams complete the endpoint transition and initial production validation in under 24 hours.
Switching your speech-to-text provider: A migration checklist for note-takers and contact center platforms
TL;DR: Switching your speech-to-text provider is a structural risk only if you skip the pre-migration audit. The real danger is not the cutover itself but continuing to run infrastructure that corrupts CRM entries, breaks LLM summaries, and inflates your per-hour cost with add-on fees you never modeled at scale. The four-stage phased cutover in this guide is designed to reach 100% production traffic without user-visible downtime, the same structural approach that let Aircall cut processing time by 95% and scale to over one million calls per week after adopting Gladia.
How decision intelligence improves customer service consistency in contact centers
TL;DR: Contact centers fail to deliver consistent service when routing infrastructure runs on static rules engines that cannot handle the complexity of real human conversation. Modern speech-to-text infrastructure addresses this by processing raw audio and feeding structured outputs to your CRM, using machine learning to analyze intent, sentiment, and speaker characteristics. Transcription accuracy sets the ceiling for every downstream action: a wrong word silently corrupts a CRM entry, a missed intent misfires a routing decision, and a misread sentiment score delays escalation. This playbook covers how to build and deploy that architecture without blowing your latency budget or your unit economics.
ASR vs. LLMs – Why voice is among the biggest challenges for AI
Published on Jan 16, 2025
When people talk about recent AI advancements, Large Language Models (LLMs) like ChatGPT often steal the limelight. They summarize, write, and generate text with impressive fluency, making them the poster child of generative AI.
But in the background, Automatic Speech Recognition (ASR) systems are grappling with a more demanding challenge—understanding and transcribing human speech with pinpoint accuracy. Unlike LLMs, ASR systems don’t have the luxury of abstraction or creativity. They operate in a world where every word, pause, and intonation matters.
So, why is ASR so much more complex than LLMs? To understand, let’s dive into the unique challenges posed by voice and how they shape the development of ASR technologies.
Ground truth vs. abstraction
The fundamental difference between ASR and LLMs lies in their approach to “truth.” LLMs are built to generate language that is plausible, coherent, and sometimes even creative. They can summarize, paraphrase, or generate entirely new ideas based on textual input. Their outputs are often evaluated based on fluency and contextual relevance, not rigid accuracy.
ASR, on the other hand, has no room for flexibility. Its primary goal is to extract the exact words spoken by a person—the ground truth. Imagine transcribing a legal deposition or medical consultation. A misplaced comma or missed word can alter the meaning entirely, and that level of precision is non-negotiable in ASR. Where LLMs can approximate or summarize, ASR must deliver perfection.
This inherent need for accuracy makes ASR far more challenging. Every punctuation mark, inflection, and name has to be captured faithfully. It’s the difference between painting a beautiful impressionist landscape and tracing the exact outlines of a technical diagram. Both require skill, but one demands far greater attention to detail.
The challenges of voice as input
Text input, the bread and butter of LLMs, is clean, uniform, and relatively easy to standardize. Sentences don’t change based on who writes them, and spelling variations are minimal. Voice, however, is a completely different beast. Human speech is highly variable and unique, influenced by accents, dialects, speech speed, tone, and even mood. Two people from the same region speaking the same language might pronounce the same phrase in entirely different ways. This variability makes it incredibly difficult for ASR systems to generalize across speakers.
Then there’s the challenge of external noise. Background chatter, poor-quality microphones, or distorted audio from telephony networks can all impact an ASR system’s ability to accurately transcribe speech. In contrast, LLMs don’t care if you’re typing on a cheap keyboard or the latest high-tech gadget—the text remains the same.
For ASR, capturing spoken words requires navigating a constantly shifting landscape. Imagine transcribing a conversation in a noisy café versus generating a summary from a neatly typed document. The former requires far more effort, and that’s the everyday reality for ASR systems.
Environmental variables
The environment plays a crucial role in ASR performance. Voice input can be affected by the quality of the recording device, the transmission medium, and even the surrounding conditions. A high-end microphone in a quiet room produces clear audio that’s easier for ASR to process. But what happens when the same speaker uses a low-quality phone on a busy street? The degraded signal creates additional layers of complexity for the system to decode.
Text-based LLMs, by comparison, are blissfully unaffected by environmental factors. A sentence typed on a laptop in a quiet library is no different from one typed on a smartphone during a bumpy bus ride. This stability gives LLMs a significant advantage in terms of reliability.
For ASR, even small variations in audio quality can dramatically affect performance. Networks like telephony systems may downsample audio to save bandwidth, further complicating the task for ASR. The same voice recorded over a landline (8 kHz sampling) and a data network (16 kHz sampling) can sound completely different to the system.
Data scarcity
One of the biggest advantages LLMs have is access to massive, well-annotated datasets. The internet is a treasure trove of text data, providing LLMs with an endless supply of material to learn from. ASR, however, faces a much tougher road. Annotated voice data is not only harder to collect but also incredibly diverse. Voices vary by gender, age, region, and even health conditions, making it nearly impossible to create a one-size-fits-all training dataset.
Additionally, collecting voice data raises ethical concerns, especially around privacy and consent. Unlike text, which can often be anonymized, voice recordings carry identifiable characteristics. This limits the availability of high-quality datasets and slows the development of ASR systems.
Infrastructure demands
Voice data is far richer than text, but that richness comes at a cost. Audio files are larger, more complex, and require significantly more processing power. Encoding differences, file corruption, and variations in sampling rates all add to the challenges of working with voice data. ASR systems also need to account for prosody—the rhythm, tone, and intonation of speech—which adds an additional layer of complexity.
Text, by comparison, is straightforward. A letter “A” is always a letter “A,” no matter where or how it appears. Voice data, on the other hand, is a continuous signal that must be segmented, analyzed, and interpreted. The computational burden of processing voice makes ASR systems far more resource-intensive than their LLM counterparts.
Richness vs. stability
While voice is undeniably harder to work with, its richness also offers unique opportunities. Voice carries emotion, personality, and context that text simply cannot capture. ASR systems, despite their challenges, can create deeply personalized and immersive experiences that go beyond the capabilities of LLMs.
For example, voice-based systems can distinguish between speakers in a conversation or infer emotional cues from tone. This richness makes voice interfaces more engaging and human-like, even if the path to achieving that level of sophistication is fraught with difficulties.
Taking stock: Voice is the future
ASR and LLMs represent two sides of the AI coin. LLMs shine in tasks that require abstraction, summarization, and creativity, while ASR excels at capturing the nuances of human speech. But make no mistake—ASR is the harder road. Its complexity stems from the variability of voice, the need for precision, and the myriad environmental and technical challenges it faces.
Despite these hurdles, ASR holds immense promise. The richness of voice makes it the foundation for more personalized, accessible, and human-centric AI applications. As technology advances, we can expect ASR systems to become even more integral to how we interact with the digital world. And while it may be harder to build, the payoff will be well worth the effort.
Let’s not overlook the quiet revolution happening in ASR. It’s not just about recognizing words; it’s about understanding people—and that’s what makes it one of the most exciting frontiers in AI.
