Voicebot

Voice AI is automation that handles spoken conversations using speech recognition, language understanding, and speech synthesis. It carries constraints text automation does not: recognition errors on names and numbers, sensitivity to accents and dialects, and a much lower customer tolerance for response latency.

IVR is an automated telephony system that plays recorded or synthesized prompts and routes callers based on their input. Touch-tone (DTMF) menus require keypad presses. Directed dialogue lets callers speak from a limited list of options. Natural language or conversational IVR interprets open-ended speech to identify intent. Success is typically measured by containment rate: the share of calls resolved without a human agent. Deep menu trees are a common source of customer friction, causing repeat prompts and abandoned calls. OnClarity’s AI agents work across chat and voice, handling voice interactions conversationally.

Word error rate (WER) measures speech recognition accuracy. It is the sum of substitutions, insertions, and deletions, divided by the total words in the reference transcript, expressed as a percentage. Lower is better. Vendor-published WER figures are only meaningful when compared on identical audio. Accents, background noise, telephony codecs, and domain-specific vocabulary all push the error rate higher in production, regardless of benchmark claims. A contact center example: a voicebot transcribes a caller’s accent clearly but consistently mishears a product or brand name. This inflates substitution errors until that term is added to a custom vocabulary list. This voice AI glossary will keep growing as new contact center infrastructure terms and voice AI terminology enter vendor conversations. Bookmark it alongside OnClarity’s main glossary of call center technology definitions and AI contact center glossary entries for the next RFP or vendor call.

Call transcription converts recorded or live call audio into searchable text using automatic speech recognition (ASR). It can run in real time, powering live agent guidance, or in batch after the call ends. Batch processing generally yields higher accuracy. Quality transcripts include speaker diarization, timestamps, and redaction of sensitive data like card numbers. Accuracy is measured by word error rate. OnClarity’s AI Agent QA uses transcripts to score 100% of conversations for QA and compliance. Example: a team searches six months of transcripts for every mention of a failed payment flow to find a root cause.

Conversational AI describes systems that understand and respond in natural language across chat, email, and voice. The category spans intent-based bots and modern language-model agents, and the practical difference between them is whether the system can only reply or can also take action in business systems.