As a Bostonian, I often hear people trying to imitate our native accent with the phrase, “Pahk tha cah in Hahvahd Yahd.” In fact, speaking with this lack of hard “r” sound is the mark of a true Bostonian. Comparatively, in a place like Dallas, their “r” sounds are emphasized much more – they might say it like “Har-verd Yar-duh.” It’s important that speech recognition systems in healthcare networks are able to understand accents in order to provide patients the best experience – but is it reasonable to expect AI to keep up? Automatic speech recognition systems (ASRs) are only as successful as what they’re trained to understand, and most aren’t trained to recognize accents beyond Standard American English. The number of accents across the US is so high it’s impossible to count and that becomes even more problematic when people say proper names. When calling to speak to “Doctor Andrea Kumar,” a Bostonian might ask for “Dahktah Ahndrayah Koomah,” while a Texan might say “Doctur Andreeuh Kummer.” Most ASR systems are poor at recognizing proper names because of a lack of training data. These systems are trained on thousands of hours of audio data, but the frequency with which any particular proper name appears in that data is extremely low by comparison to the rest of the language. There are at least 3 million different surnames in the US, and the potential pronunciation variations of each name makes recognition even more challenging. However, health systems lose money when phone systems can’t understand what callers are saying. People end up frustrated and operators remain overwhelmed with calls.
Speech recognition systems that can’t recognize accents prevent the business optimization that AI is meant to create. Those systems are not decreasing call volumes to operators, nor easing operational burdens for patient access centers. When conversational AI technology is not surrounded by tools and applications that improve recognition of local pronunciations, it does not produce the best ROI. Health systems must confront the challenges that accents pose, especially when dealing with proper names. This means implementing intelligent speech solutions that account for the different voices in regional calling communities. Parlance employs a variety of techniques that produce superior name recognition at hundreds of hospitals and clinics across the nation.
In order to get maximum return on investment for any speech-driven solution, it’s important to remember that improving performance requires more than just technology. That’s why Parlance delivers every engagement as a fully managed service; continuously learning and adapting to each health system and its callers – including their accents. Implementing voice-driven AI solutions that can better understand regional accents, dialects, and pronunciations requires both experience and adaptability. Parlance is the very best! Our proprietary applications and time-tested tools have evolved through many generations of speech technology. We ensure that it’s easy for callers to access the people, departments, and resources they need. We know what it takes to meet callers where they are, rather than at the limits of a given ASR engine’s capability.
At Parlance, we have always believed that callers deserve friction-free, voice-driven access to the right resources inside large organizations. Our continuous innovation is driven by this mission that has guided our pioneering work to modernize caller experiences.
By Annmarie Block
