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Factors affecting the accuracy of speech-to-text transcripts
TL;DR: Production STT accuracy fails not because of model benchmarks, but because of the gap between studio evaluation audio and the messy, multilingual, overlapping speech real users produce. Four root causes drive that gap: input audio quality, speaker traits (accents, code-switching, and overlap), domain vocabulary deficits, and model training data diversity. WER alone doesn't capture production risk. Semantic accuracy and Diarization Error Rate matter just as much when CRM syncs, coaching scores, and AI summaries all depend on what the transcript gets right. Solaria-1 delivers on average 29% lower WER on conversational speech and 3x lower DER compared to alternatives, benchmarked across 7 datasets and 74+ hours of audio with open, reproducible methodology.
Business call transcript analysis techniques for sales and support teams
TL;DR: Upstream transcription errors compound through every downstream system: LLMs, sentiment models, and CRM pipelines are only as reliable as the transcript they process. Core conversation intelligence techniques, including sentiment scoring, BANT extraction, objection mining, and talk-ratio analysis, all depend on transcription quality. Async/batch processing provides full conversation context, making it the right default for post-call workflows.
TL;DR: Caller intent routing fails at the transcription layer long before it fails at the NLU layer. If ASR misreads "cancel" as "candle" due to background noise or a non-native accent, no downstream classifier recovers the routing decision. This article covers the full intent pipeline: ASR, NLU, classification, and routing execution, the latency budgets that constrain real-time systems (~700ms total), and the audio conditions that break most production deployments.
AI-powered healthcare assistant enhances medical transcription by 120% with Gladia
Published on Feb 28, 2025
Medical transcription is among the most critical and challenging verticals for ASR models to date.
Filled with drug names and medical jargon, medical consultations, dictations, and online conferences require versatile solutions, with custom vocabulary and specialized models needed to make speech-to-text solutions attuned to jargon. There’s the issue of security too, as audio from medical consultations is among the most sensitive confidential data out there.
A fast-growing healthcare generative AI startup, who prefers to remain anonymous, turned to Gladia for top-quality medical transcription at scale. Here’s how we helped them increase their accuracy and speed of transcription, all while ensuring 100% security of confidential user data.
Challenge
Doctors spend about 60% of their time on computers, doing non-clinical work. This startup is aiming to get that number to 15%, enabling doctors to allocate most of their time for consultation, diagnostics, and other high-value tasks with the help of AI.
They knew that having accurate transcription for note-taking during consultations was the first step in designing a holistic solution to achieve this milestone.
Indeed, the platform’s ability to understand and actively transcribe jargon-filled medical conversations is an essential prerequisite for LLM-powered notes, prescriptions, and intricate EHR enrichment that distinguish their AI co-pilot.
Speed is likewise a key factor for them, as the ability to generate notes shortly after the consultation is critical for efficient clinical workflows.
Moreover, they needed to ensure 100% protection of all user data in accordance with HIPAA and GDPR, which most of the US-based providers are generally not able to provide.
This is why their team took the task of choosing a speech-to-text provider very seriously. With regular evaluations in place, they have tested over 7 different providers before, including the Big Tech cloud solutions — all of which ultimately failed to strike the right balance between accuracy, speed, price, and security standards.
Following a swift onboardingwith our tech team, they began to use Gladia as its primary speech-to-text provider. The results did not take long to show.
By working with the Gladia team to iterate and scale up, they saw a noticeable impact on their system’s performance:
The team was likewise impressed by the quality of Gladia’s technical assistance, allowing them to not only set up their dedicated environment in a matter of hours but also benefit from Gladia’s in-house engineering expertise to optimize their infrastructure as a whole.
Given the initial success with Gladia API and its on-premise deployment, this innovative company is already considering how they will leverage our product in the future as they extend their platform to new stakeholders.
For instance, they look forward to experimenting more with multilingual transcription and translation, which would enable patients to consult physicians in their native language. They also intend to leverage speaker diarization for collective medical meetings.
About Gladia
Gladia provides a speech-to-text and audio intelligence API for building virtual meeting and note-taking apps, call center platforms, and media products, providing transcription, translation, and insights powered by best-in-class ASR, LLMs and GenAI models.
Having read this case study, do you feel like Gladia could be the right fit for your business too?
