In the voice technologies discipline, speech recognition has the potential to completely transform the customer experience. According to Adobe, over 4 in 10 product search journeys originate from voice and speech recognition could also automate a large part of customer support. Interestingly, despite having been around for several decades, speech recognition is an evolving technology – yet to reach full maturity or 100% accuracy. What does this mean for CX?
Let’s find out.
What is Speech Recognition and How Does it Work?
You can define speech recognition as a technology (or a set of technologies) that accept human voice as input, process this raw audio into structured text, and generate some kind of output which could be either a transcription of the text, and analysis, or an automated action). Unlike voice recognition that seeks to match a series of uttered sounds to a pre-defined speaker, speech recognition is dedicated to converting human-generated audio into structured text.
The efficacy of speech recognition depends on how accurately the system can recreate what’s being said. This is harder than it sounds – every human being has their own unique inflexion, tonality, and manner of speaking, almost like a fingerprint. So, it is difficult to translate every speaker’s audio into text with equal accuracy. Also, language is an ever-evolving entity, and machines aren’t always capable of matching audio to meaningful words given the expansive nature of human vocabulary.
Speech recognition tries to overcome this by training its core algorithm on as large and diverse a dataset as possible, feeding it every possible kind of utterance, and its corresponding translation. Advanced AI algorithms are what makes speech recognition more or less accurate today, maturing well beyond its simple, phonetic sound analysis capability from the 1950s.
Key Features of Speech Recognition
- Speech recognition isn’t 100% accurate – Speech-to-text accuracy ranges from 70% to 85%, which means there could be around 15 errors for every 100 words uttered. Providers are constantly working to improve this, with Google coming out with a new speech recognition system as recently as November 2020
- Speech recognition uses artificial intelligence – In order to process such a massive volume of input data and generate coherent results, AI intervention is necessary. It also enables the algorithm to learn from every new input and text generation cycle using machine learning techniques
- Speech recognition isn’t a standalone technology – It has to be integrated with a user-facing UI, self-service tools, data process & recording systems, etc., to be truly viable as an application
Benefits of Speech Recognition
Despite a few shortcomings, speech recognition continues to be an enormous area of interest and investment. This is because it has the potential to revolutionise customer experiences, from enabling hands-free interactions for field support agents to intelligent IVR that actually recognises what you’re saying. Speech recognition can also extract valuable insights from the hundreds of telephonic conversations taking place in your contact centre every day.
Finally, voice search, powered by speech recognition, will lead to a new kind of customer journey, compelling organisations to redefine their marketing funnel accordingly.
