TL;DR
- Most major conversation-intelligence tools were trained on English. On spoken Hebrew they reach 70%–85% transcription accuracy, not enough for reliable analysis.
- The difficulty is more than language: call-floor slang, overlapping speech, Hebrew-English switching mid-sentence, diverse accents.
- Below 90% accuracy, call scores and compliance checks become guessing. Hebrew-first tools reach over 95%.
- The only test that counts: run the tool on your own real calls and read the transcript against the recording.
On paper, every international tool "supports Hebrew." In practice, a manager running real call-floor recordings finds transcripts with holes, sentences attributed to the wrong speaker, and analysis that misses the point. This gap is not a bug, it is architecture.
Why Hebrew is hard for generic models
- Training data: large speech models saw orders of magnitude more English. Spoken Hebrew barely exists in their data.
- Rich morphology: one Hebrew word folds prefixes, gender and tense together. A one-letter error changes the meaning.
- Code-switching: mixing Hebrew and English in one sentence is normal on an Israeli call floor and a nightmare for monolingual models.
- Pace and overlap: Israeli sales calls are fast, with interruptions that strain speaker separation.
What happens when transcription is weak
Transcription is the base everything rests on. At 85% accuracy, one word in seven is wrong. Script-adherence checks miss clauses that were said and flag ones that were not. Half the objections are never detected. The call score turns from a metric into a guess, and the team stops trusting the system within a week. Rule of thumb: below 90% accuracy automated analysis does more harm than good, because it produces false confidence. Above 95% you can build QA, scoring and coaching on it.
What it takes to do it right
A tool that truly works in Hebrew is built Hebrew-first: a speech engine trained on real spoken Hebrew calls, diarization calibrated for Israeli speech pace, and an analysis model that knows local call-floor phrases. Saleso was built exactly this way and transcribes Hebrew and English at over 95% accuracy.
How to test a tool before buying
- Take 5 of your real recordings, including a noisy call and a heavy accent.
- Compare transcript to recording, sentence by sentence. Count meaning-changing errors.
- Check speaker separation: does the system know when the rep spoke and when the customer did?
- Ask for analysis against YOUR script, not a generic demo report.
Frequently asked questions
What transcription accuracy should we demand in Hebrew?
Over 95% on your own real calls. Below 90%, any analysis built on the transcript is unreliable for QA or scoring.
Do Gong or Chorus work in Hebrew?
The large international tools were built for English, and their spoken-Hebrew support is partial. Before deciding, run them on your own recordings and compare transcripts, sentence by sentence, against the audio.
What about calls that mix Hebrew and English?
Code-switching is the hardest test case. A tool trained on real Israeli calls handles it by default, because that is how an Israeli call floor talks.