Speech recognition is a booming market, expected to reach $26.8 billion globally by 2025. It promises to garner meaningful insights from customer’s voice. It could dramatically improve your automation capabilities, using voice assistants to respond to customer queries in natural languages with little to no manual intervention. At the core of this technology is the science of automated speech recognition or ASR, which converts human speech from a series of disconnected sounds into a textual string that’s comprehensible to human beings.
What is ASR?
ASR can be defined as a field related to computational linguistics that deals with the recognition and translation of natural spoken languages such as English, Hindi, etc., into a textual string in a human-readable format.
There are two types of ASR engines – speaker-dependent and speaker-independent. The former type can only understand the words and phrases being said, but can also verify if the speech matches with a designated speaker. Speaker-dependent ASR analyses spoken words to only unearth meaning and patterns, without any cognition of the speaker’s identity.
How Does Automated Speech Recognition Work?
ASR relies on artificial intelligence (specifically neural networks, machine learning, and deep learning), to accurately convert speech to text. Neural networks are trained on different sound-word classifications, gradually increasing in complexity. Machine learning helps to refine results and augment the ASR engine’s capability over time. Deep learning helps to enable unsupervised AI model training, where the ASR engine should be able to learn entirely by itself, by repeated trial and error.
To start using ASR, you can leverage any of the speech-to-text APIs available in the market today. Google has a powerful ASR tool, ideal for transcribing both real-time and historical conversations. It gives you the option to choose from industry-specific ASR engines, and you could host the application either on-premise or on the cloud. Deepgram is another enterprise-grade speech platform that offers advanced ASR capabilities. Using Deepgram, you get to train your own AI model, although you do not need AI expertise to get started.
How Can Contact Centres Gain from ASR?
ASR has three broad application areas – data entry, system commands, and access/authentication. All three task types are extremely relevant in a contact centre scenario, which is why ASR has recently come under the spotlight. Field service providers could use ASR to control equipment when their hands are occupied. ASR can provide detailed and accurate call transcriptions, which can inform analytics and agent training programs. ASR allows customers to confidently request payments, cancellations, data modifications, etc., with very little risk of fraud.
Today, small vocabulary ASR systems that recognise a limited set of words like yes, no, maybe, etc., are widely used in contact centre environments.
One Concern Around ASR Technology
A 2020 academic study suggests that ASR might be discriminatory towards specific languages and linguistic intonations, as per an analysis of Amazon, Apple, Google, IBM, and Microsoft’s ASR systems. This highlights the need to rigorously train our AI models before implementing ASR at scale.
