May 20

The importance of data in voice assistant development 

It is undeniable that Artificial Intelligence (AI) has been changing our lives for the better and one of its famous abilities is to bring natural conversations to life. People have been familiar with chatbots for a while now, but voice assistants can still be a relatively new concept for some.  

In this article, with consultancy from Dr. Thomas Forss – co-founder and CEO at StageZero Technologies, you’ll learn about the concept of voice assistants and go behind the scenes of one of our customers who are specialists in this field. We will also share with you our most crucial points to consider regarding the training data needed for the development of your voice assistant model. 

What is a voice assistant? 

Maybe you’re already familiar with Siri by Apple, Alexa by Amazon, or Google Assistant. These are all examples of voice assistants. Most of the voice assistants consist of four different components. The automated speech recognition component that translates the speech we provide into text. The Natural Language Processing component trains the machine to understand what we are saying and identify commands and intents. The software assistant within the voice assistant processes different commands or intents. And finally, the text-to-speech component allows the voice assistant to talk and to respond to what we are asking. 

a woman speaking to her phone to voice command

Then, there are two types of voice assistants: conversational, and command-based. Examples of the conversational type are, again, Siri or Alexa, where we can have conversations with the AI, which mimics human interaction. This is what we’ve been familiar with from movies and real life: we have a conversation with the AI, and it instantly answers the questions we ask. The other, command-based voice assistants don’t allow conversation. Instead, you just tell the ‘assistants’ what to do and they execute what you want!  

How StageZero helped our customer to improve their voice assistant solutions 

One of StageZero’s customers has been developing a voice assistant specifically for delivery drivers. Their voice assistance solutions aim to augment the daily workflows of mobile workers, deployed as driver apps and in scanners or vehicles. For this project, we used our own unique technology to provide the customer with speech recognition training data. This data is used in their machine learning processes to empower their AI systems.  

This is a typical case for StageZero as we help businesses collect, annotate, and validate data. It’s always important that the data from the collection process be diverse. For annotation, it depends on the case: in some cases, you need a full transcription on the data and in other cases, you may need to use an intent classification. Finally for validation, we ensure that the data that enters the system is correct and meets the required quality standards. We even provide model development in cases where the teams don’t necessarily possess the relevant capacities in-house. 

person's hand using a smartphone while driving car to use voice assistant

Voice assistants and data 

When it comes to data for voice assistants, there is famously a lot to consider. Having been working with voice assistants for many years now, we have three important key take aways for you when considering data for voice assistants: 

The first point is basically ‘common sense’ in the AI development field but may also be the most important point: for it to be usable, the data should be representable of the end-user conditions. In other words, the data should be diverse enough to cover all the users that are going to be using your service. Examples include age range, gender, nationalities, languages, dialects, etc. 

Secondly, quality is more important that quantity. It’s more critical to have unambiguous labels than to have a lot of data. Unambiguous labels help you ensure that there is no overlap, especially if you have a smaller data set. If you have a larger data set, there might be less problems - however, it is still ideal to have both unambiguous labels and a large amount of data.  

The final advice for you to consider is: when you start developing a voice assistant using an iterative approach, either from a software development perspective or from a data perspective, you may benefit from starting out small and learning from the first batches. Once you have learnt what you need more of, you can then expand the scale of the data set. This way, you will save a lot of money when developing. 

Would you like to learn more about how StageZero Technologies can provide training data for your voice assistant model? Speak to our partnership team here. 

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