English for Speech Recognition Developers

Speech recognition engineering has a vocabulary shaped by both signal processing and language modeling, and it comes up constantly in discussions with product teams building voice assistants, transcription tools, and call center automation. Precise terms like “word error rate” and “diarization” let you describe exactly what’s failing when a voice feature misbehaves, instead of vaguely saying “the transcription is bad.”

Key Vocabulary

ASR (Automatic Speech Recognition) The general term for technology that converts spoken audio into text. Example: “Our ASR pipeline handles the first pass of transcription before we run any downstream language understanding on the text.”

Word error rate (WER) The standard metric for measuring ASR accuracy, calculated from the number of substitutions, deletions, and insertions needed to transform the model’s output into the correct reference transcript, divided by the total number of words in the reference. Example: “WER on this accented speech test set is much higher than on our standard benchmark — around 18% versus 6%.”

Wake word / hotword A specific word or phrase (like “Hey Siri”) that a voice device continuously listens for locally, triggering full speech recognition only after it’s detected. Example: “The wake word detector is running on-device to keep latency low and avoid streaming audio to the cloud until it’s actually needed.”

Diarization The process of determining “who spoke when” in an audio recording — segmenting the audio by speaker, even without necessarily identifying who each speaker actually is. Example: “Diarization correctly separated the two speakers on this call, but it mislabeled a brief interruption as a third, nonexistent speaker.”

Voice activity detection (VAD) A technique for distinguishing segments of audio that contain speech from segments that are silence or background noise, typically used before or during ASR to reduce unnecessary processing. Example: “VAD is cutting off the first syllable of some utterances — we need to add a small buffer before the detected speech start.”

Acoustic model The component of a speech recognition system that maps raw audio features to likely phonetic units, as distinct from the language model, which predicts likely word sequences. Example: “The acoustic model handles background noise reasonably well, but errors increase sharply with heavy cross-talk.”

Endpointing Determining when a user has finished speaking, so the system can stop listening and begin processing the utterance, balancing responsiveness against cutting users off mid-sentence. Example: “Our endpointing is too aggressive — it’s cutting users off during natural mid-sentence pauses, especially with longer, thoughtful responses.”

Barge-in The ability for a user to interrupt a voice assistant’s spoken response with a new command, requiring the system to stop talking and start listening again immediately. Example: “Barge-in isn’t working reliably on this device — users have to wait for the assistant to finish speaking before their interruption is recognized.”

Common Phrases

In code reviews:

• “This endpointing threshold is hardcoded at 800 milliseconds — we should make it configurable, since different languages have different natural pause lengths.”
• “We’re not handling overlapping speech in the diarization step, so cross-talk segments are getting silently dropped from the transcript.”
• “The wake word model has a high false-accept rate on background TV audio — we should include more negative training examples from television and radio.”

In standups:

• “Yesterday I retrained the acoustic model with more accented speech data; today I’m measuring WER improvement on our diverse-accent test set.”
• “I’m blocked on a barge-in bug — the microphone isn’t reopening fast enough after the assistant starts speaking, so early interruptions get missed.”
• “I finished tuning the VAD sensitivity; it’s now catching quiet speech onset without also triggering on typical background noise.”

In product/UX discussions:

• “If endpointing is too fast, users feel cut off; if it’s too slow, the assistant feels sluggish — we need to find the right latency tradeoff for this use case.”
• “Diarization accuracy matters a lot for call center transcripts, since agents need to know exactly who said what during a compliance review.”
• “We should surface a lower-confidence transcript differently in the UI, rather than presenting every ASR output with equal certainty.”

Phrases to Avoid

Saying “the transcription is bad” without specifying the failure type. Say instead: “WER is elevated on this test set” or “the model is substituting similar-sounding words” or “diarization is misattributing speaker turns.” Each points to a different subsystem and a different fix.

Saying “it didn’t hear me” for every voice interaction failure. This could mean several different things: VAD failed to detect speech onset, the wake word wasn’t recognized, or endpointing cut off the utterance too early. Naming the specific stage speeds up debugging significantly.

Saying “make it more accurate” without specifying the dimension. Accuracy in ASR can mean lower WER generally, better performance on accented speech specifically, better handling of domain-specific vocabulary, or better noise robustness — each requires a different data or modeling strategy.

Quick Reference

Key Takeaways

• Use word error rate (WER) as the standard way to discuss ASR accuracy, and specify which test set or condition it was measured on.
• Distinguish the specific pipeline stage (wake word detection, VAD, endpointing, ASR, diarization) when describing a voice interaction failure.
• Avoid vague phrases like “it didn’t hear me” or “the transcription is bad” — name the subsystem responsible.
• Endpointing and barge-in are UX-critical concepts that connect directly to user-perceived responsiveness — frame them in product discussions accordingly.
• When discussing accuracy improvements, specify the dimension (accent robustness, domain vocabulary, noise handling) rather than saying “more accurate” generically.