What is Automatic Speech Recognition?

Automatic Speech Recognition (ASR), also known as Speech-to-Text (STT) technology, is a branch of artificial intelligence (AI) and natural language processing (NLP) that focuses on converting spoken language into written text.

Automatic speech recognition technology enables voice interaction with computer applications, eliminating the need for manual data input via a keypad. For instance, ASR eliminates the requirement for callers to “press one” when contacting customer service by converting spoken words into text or computer commands.

Within a contact centres you can integrate ASR with Interactive Voice Response (IVR) systems to enhance the customer experience. It allows callers to perform self-service tasks like checking account balances and verifying their identity before connecting with an agent. ASR is also invaluable for determining the purpose of the call and directing it to the appropriate agent.

How does ASR work in a contact centre?

Automatic Speech Recognition is achieved through a multi-faceted process that combines sophisticated algorithms, statistical models, and machine learning techniques. Here is how a brief overview of how it works.

Audio Input: ASR systems begin by receiving an audio input, typically in the form of spoken language captured through a microphone or telephone line. This audio signal serves as the raw data to be transformed.

Acoustic Analysis: The ASR system performs an in-depth acoustic analysis of the audio input, dissecting it into smaller segments, such as phonemes or sound units. This analysis involves examining various aspects of sound, including pitch, intensity, and duration.

Language Modelling: Concurrently, the ASR system employs language modelling techniques to predict and anticipate the most likely words or phrases that correspond to the observed acoustic patterns. These language models draw upon extensive datasets to enhance prediction accuracy.

Decoding: The ASR system employs complex decoding algorithms to match the acoustic analysis with the language model predictions. It identifies the sequence of words or phrases that best align with the audio input, ultimately generating a transcription in written text.

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