# Which speech analytics tools work reliably with heavy accents and technical terminology in specialized industries?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Generic transcription accuracy numbers rarely hold up once you introduce regional accents, code-switching, or an industry's own jargon (drug names in a pharmacy line, part numbers in an industrial support queue). <strong>What we're hoping to find:</strong></p><ul>
<li>Custom vocabulary or terminology training, not just a fixed dictionary</li>
<li>Documented accuracy across accent variation, not a single blended benchmark</li>
<li>A way to correct or retrain the model when it consistently mishears the same term</li>
<li>Enough transparency in the transcript to tell where the model guessed versus was confident</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">A few <a class="a a--md" elv="true" href="https://www.g2.com/categories/speech-analytics">speech analytics platforms</a> come up when this specific problem is the frame:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/verint-speech-and-text-analytics/reviews"><strong>Verint Speech and Text Analytics</strong></a><strong>:</strong> Its Exact Transcription Bot is built around speaker-separated transcription at scale, which is a different bar than transcribing a single clean line.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/callminer-eureka/reviews"><strong>CallMiner Eureka</strong></a><strong>:</strong> Analyzes interactions at a granular level and is used across the pharmaceuticals, healthcare, and manufacturing industries, which have dense internal terminology.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/talkdesk/reviews"><strong>Talkdesk</strong></a><strong>:</strong> Supports industry-specialized use cases across healthcare and financial services specifically, which usually means the vocabulary tuning has been through that gauntlet already.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Has anyone actually tested one of these against a call center with a genuinely multi-accent workforce, and did the vendor's terminology customization keep up without a lot of manual correction?</p>

##### Post Metadata
- Posted at: 6 days ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;The key test is whether accuracy stays stable on &lt;em&gt;your&lt;/em&gt; accent mix and terminology after tuning, not whether the vendor reports a strong aggregate transcription score. I’d ask for a pilot using real calls and measure repeated term errors, confidence visibility, and how quickly custom vocabulary changes improve the transcript.&lt;/p&gt;

##### Comment Metadata
- Posted at: 4 days ago
- Author title: Marketing Executive





## Related discussions
- [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team)
  - Posted at: over 13 years ago
  - Comments: 6
- [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section)
  - Posted at: over 13 years ago
  - Comments: 0
- [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm)
  - Posted at: over 13 years ago
  - Comments: 4