Is it just us or are virtual assistants actually becoming quirkier and sassier by the day? If you remember your first interaction with a virtual assistant like Siri, Cortana, or Alexa, you would recollect bland responses and plain execution of tasks.
However, their responses are not the same they used to be. Over the years, they have grown to become sarcastic, witty, and in simple words — more human-like. It’s like they are just a step away from cracking the Turing Test. But this has been a journey, hasn’t it?
To get here, close to a decade of AI training has happened at the backend. Thousands of data scientists and AI experts have meticulously worked for hours together to source the right datasets to train their speech projects, annotate key aspects and make machines learn them intact. From tagging parts of speech to teaching machines quirkiness and funny responses, tons of complex tasks have happened in the development phases.
But what is the process actually? What does it take for experts to train and develop speech projects? If you’re working on a speech project, what are the factors you need to keep in mind?
One of the first steps in training speech modules is to understand how your audience will interact with them. Work on getting insights on what they would say to activate your speech module, use it through dictation, and listen to results. So, in this case, know the triggers, responses, and output mechanisms.
For this, you need to collect massive volumes of representational data that are accurately close to your source. From call transcriptions to chats and everything in between, use as many volumes of data as possible to zero in on these crucial aspects.
Once you have a general understanding of how your audience will interact with your speech module, realize the specific language they would use that is in line with your domain of operation. For instance, if your speech project is for a mhealth application, your system needs to be familiar with healthcare jargon, processes, and diagnostic phrases to accurately do its job. If it’s a project for an eCommerce solution, the language and the terms used would be completely different. So, know the domain-specific language.
By now, you have a compilation of phrases, sentences, and text of value with you. Now, you need to turn these into a solid script and record it from humans for your machine learning modules to understand and learn. In every piece of recording, you could ask recorders to specify their demographics, accent, and other useful information you could use as metadata during data annotation.
How accurately your speech module responds to triggers depends on your recording data. Meaning, it should have data from your actual target audience. Using the same example of mhealth application, if it’s a specialized module for the elderly, you need to have data recorded from older people for your module to understand precisely.
Their accents, the way they talk, diction, pronunciation, modulation, and command are all different from people who are younger than them. That’s why we mentioned that your data should be as close to your source.
Depending on your domain and market segment, collect as much data as possible. Compile call recordings, schedule real-time recordings from people, crowdsource, approach training data service providers and do more to get datasets.
Your contributors are not trained professionals (mostly). When they talk, there are bound to be some mistakes such as the use of errs and umms. There could also be instances of repeating words or phrases because they couldn’t get them right the first time.
So, manually work on eliminating such errors and transcribe your recordings. If manual labor sounds too much like a task, use speech-to-text modules. Save them as documents with proper naming conventions that accurately define the type of recording.
You have a good source of speech data with you now. With the data you compiled in step 2 and with the actual recordings and transcriptions, you can trigger the training process for the development of your speech module. As you train, test your module for accuracy and efficiency and keep making iterations for optimization. Do not let go of errors because it takes another round of training. Fix all loopholes, gaps, and errors and make way for an airtight module in the end.
We understand that this could be quite overwhelming at first. Speech modules require complex efforts over a period of time to train conversational AI / virtual assistants. That’s why such projects are tedious as well. If you find this too technical and time-consuming, we recommend getting your datasets from quality training data vendors. They would source the most relevant and contextual data for your project on time that are machine-ready.
Social Media Description: Sourcing quality data for speech projects is tough. You need to know your audience, how they speak, how they access solutions, and more to develop an airtight solution. For those of you getting started with a speech project, here are effective steps on how you could approach data sourcing.
Description: Acquiring data for speech projects is simplified when you take a systematic approach. Read our exclusive post on data acquisition for speech projects and get clarity.
Author Bio Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
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