Diversity, equity, and inclusion (DE&I) have been top of mind in recent years. One
study even showed a 16.2% increase in hiring more diverse candidates for executive positions between January 2020 and February 2021. While hiring practices, workplace policies, and training are all important parts of DE&I initiatives, so is the use of technology.
Technology helps organizations support and scale DE&I efforts. For example, it can help identify and remove unconscious bias from hiring and promotion decisions, create equitable pay practices, track progress toward DE&I goals using analytics, and improve development and advancement opportunities for underrepresented employee groups. As individuals
resign from their jobs in record numbers, company leaders must prioritize these initiatives and draw talent back to their organizations.
Forward-thinking companies must develop innovative solutions to broaden inclusivity. Automatic speech recognition (ASR) engines are a technology businesses can use to drive DE&I strategies to achieve company goals. However, there are formidable challenges to this approach. For example, one recent
study indicated that modern ASR algorithms struggle to recognize the accents from certain world regions. Speech recognition programs' inability to understand many communities, particularly marginalized communities, is a pressing social and economic problem today.
Aside from the need for ASR providers to expand the amount of training data to build language models, some of these technologies can also better understand a much more diverse base of speakers by using deep neural networks (DNN). With traditional ASR technology, there’s (1) the acoustic input of words and phrases in a language and (2) a text representation of that input through the use of a language’s lexicon. In this traditional process, the language builds upon the acoustic input and not on the text. As such, every dialect within a language requires its own model.
With a modern DNN, language models get created from a comparatively larger source of data that includes many acoustic dialectal variations and the text associated with them. By utilizing this end-to-end DNN approach, the ASR language model understands many pronunciations for the same word. Therefore, a single language model can understand all dialects associated with a given language, eliminating inherent biases. Ultimately, the more robust the data used for the ASR technology, the higher its accuracy rates will be. As a result, companies can offer inclusive communications capabilities for a diverse set of users.
What’s more, augmenting this state-of-the-art ASR capability with additional voice-enabling technologies such as speech and language tools, voice biometrics, and call progress analysis is dramatically expanding the use of voice services to support an increasingly large number of applications.
One enhancement to ASR technologies is transcription capabilities—sometimes referred to as speech-to-text. Transcription can help organizations make content accessible for people with auditory impairments (people who are deaf or hard of hearing). Some ASR transcription technologies have delivered such a high level of accuracy that it vastly speeds up the process of generating accurate, readable content.
Another way ASR engines can drive inclusivity is through text-to-speech. For example, text-to-speech (TTS) can help people with visual impairments (i.e., blindness or bad eyesight) to easily navigate websites and content by using their auditory senses and by “listening to” words written on websites.
Speech is one of the most efficient ways for individuals to communicate with each other and with businesses. Other asynchronous communications (i.e., text) are less intuitive, making it prudent for companies to invest in technology that accurately understands a broad spectrum of languages and dialects through a broad array of channels (i.e., telephone, mobile device, web interface, etc.). With performance and accuracy as top priorities,
LumenVox unveiled its
new ASR engine earlier this year.
LumenVox built its ASR engine on a foundation of artificial intelligence (AI), machine learning (ML), end-to-end DNN architecture, and state-of-the-art speech recognition. The LumenVox ASR engine provides accurate transcriptions, regardless of language dialect. In addition, LumenVox’s ASR engine accelerates the process of adding new languages and provides a modern toolset to expand the language model to serve more applications. Companies and developers can utilize LumenVox’s ASR engine to deliver voice capabilities and modernize business communications.
Companies are evolving rapidly and enabling more applications with voice technology to deliver powerful customer and employee experiences. Innovative ASR engines allow enterprises to serve a vast and diverse user base. Business leaders that embrace modern, inclusive, and accessible ASR technology, such as LumenVox, can ensure key stakeholders reach their DE&I goals.
Download the latest white paper to learn more about the LumenVox ASR engine.
