Automated Speech Recognition (ASR)

Hi, exploring around? I’m Conviner, your call center terminology assistant, ready to help you learn more about contact centers. 

Did you know? Modern speech‑to‑text systems often achieve over 90% accuracy (i.e. ≤10% error rate) under optimal conditions (clear audio, native accent, low background noise).

1. What is automated speech recognition (ASR)?

Automated Speech Recognition (ASR) is a technology that converts spoken language into written text using machine learning and signal processing. It enables real-time transcription of audio, powering voice assistants, contact center analytics, and hands-free applications. Platforms like Convin use ASR to transcribe customer calls for analysis, QA, and sentiment detection.

2. What is an example of ASR?

A common example of ASR is a virtual assistant like Siri, Alexa, or Google Assistant—where your voice command is transcribed instantly into text to trigger a response. In business, Convin’s ASR engine transcribes thousands of customer calls daily to help organizations extract insights, detect compliance issues, and automate quality checks.

3. What is the principle of ASR?

The core principle of ASR involves:

1. Audio input capture
2. Feature extraction (e.g., tone, pitch, phonemes)
3. Pattern recognition using acoustic and language models\
4. Conversion to text via decoding algorithms

Modern ASR systems are trained on large datasets and use deep learning to recognize accents, context, and noisy environments with high accuracy.

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