As toddlers we begin talking between 12 to 18 months of age. Speech soon becomes the most natural way to communicate, but it’s only in the last few years that people have grown accustomed to issuing verbal commands to their phones and other devices. Using voice to interact with technology has grown considerably in recent years with over 70 percent of people preferring to conduct searches on the internet with voice commands. The following is an overview of speech AI technology and how it can benefit your business.
A breakdown of speech AI solutions
Speech AI is a subset of conversational AI that enables people to talk to technological entities such as chatbots and virtual agents. Speech AI applications include: speech recognition, also known as automated speech recognition (ASR) or speech-to-text, and text-to-speech.
Speech recognition enables humans to naturally interact with smartphones, computers, and other devices—either in a more directed format or in a manner that is more conversational and open-ended.
- Interactive voice response – Speech recognition enables customers to more easily navigate phone trees. Rather than potentially talking in loops with customer service agents, they can verbally respond to a series of well thought-out questions using short, simple responses.
- Natural language – Unlike interactive voice responses, natural language frees up customers to respond to questions as if they were talking to an actual person. Natural language processing algorithms interpret what the customer says.
In addition to serving as virtual assistants and customer agents, speech recognition can be used for live captioning of events, voice dictation services, and voice commands on apps like social media platforms. Other scenarios that are emerging include interactive learning and the creation of digital “humans” for entertainment purposes.
Historically, text-to-speech technology generated stilted voices that were decidedly unnatural. Today, solutions are based on deep neural networks that create more natural sounding synthesized speech, and with as little as 30 minutes of audio you can begin training your own custom voice model.
Microsoft and Google both offer online text-to-speech services for companies wanting a particular type of voice to represent their organization. You can choose from a multitude of different voices or create the voice of your choosing.
Typical examples of how text-to-speech is used include companies that create “read aloud” versions of product manuals to engage with customers and offer a more convenient way of learning about their products. Another way in which text-to-speech is used is in reading on-line course curriculum out loud so people with disabilities such as dyslexia or vision impairment have an easier time learning.
How to power and train your speech AI solution
Creating a believable, dependable, and beneficial speech AI experience for customers can be a challenge. Among the pitfalls are the frequency of word errors, latency while an AI is synthesizing its response, and the scalability required to support numerous languages, accents, and dialects. Overcoming these challenges starts with collecting data and training your algorithm.
Depending on your business need, there are two types of algorithms you can use to power your speech AI solution:
- Statistical algorithms predict the frequency of particular words and rely on a list of key words or named entities to decipher what a person says.
- Deep learning algorithms are more accurate, less expensive in the long-run, and better at understanding variations in language (such as accents and dialects).
Though less accurate and flexible, statistically-based algorithms are relatively straightforward and may be sufficient for many business scenarios. However, if you need a speech AI solution that can be trained to operate in an industry setting, such as a factory floor, consider using a deep learning algorithm. Not only do deep learning models have more potential for learning industry-specific terminology and working conditions, but they are also more adept at accurately interpreting language in noisy environments.
Also note that speech AI solutions powered by deep neural networks require massive training data sets and, potentially, months of training. Collecting the data required for such applications can be a challenge, in which case, publicly available datasets like LibriSpeech, Multilingual LibriSpeech, or Google Speech Commands are a good option for getting started.
Though publicly available datasets like this may take care of the heavy lifting, your speech model still needs additional training to address the specific nuances of your brand, product, industry, or customer needs. This is where custom data collection comes in. Using first-hand knowledge of your company, customers and market, you can develop a list of key terms, phrases and other elements of speech that are essential to building a customized speech AI solution. From that list, you can build out and execute a plan for collecting the data, labeling and organizing it, and training your algorithm. Finally, you can do this across multiple languages and dialects to reach a wider target audience.
Realizing the benefits of speech AI
Speech AI promises to remain in high demand as an easier way for people to use technology. By harnessing ASR and text-to-speech, your company can respond more rapidly to customers, provide more efficient and natural interactions, generate real-time insights, and bridge the accessibility gap for people with reading or hearing impairments.
LXT can design a data program specific to support your speech AI solutions. Contact us today at email@example.com to discuss your needs with one of our experts.