Voice assistants might be these cool, predominantly female voices that respond to your requests to find the nearest restaurant or the shortest route to the mall. However, they are more than just a voice. There is a high-end voice recognition technology with NLP, AI, and speech synthesis that makes sense of your voice requests and acts accordingly.
By acting as a communication bridge between you and the devices, voice assistants have become the tool we use for almost all our needs. It is the tool that listens, intelligently predicts our needs, and takes action as required. But how does it do this? How do popular assistants like Amazon Alexa, Apple Siri, and Google Assistant understand us? Let’s find out.
Here are a few voice-controlled personal assistant statistics that will blow your mind. In 2019, the total number of voice assistants globally was pegged at 2.45 billion. Hold your breath. This number is predicted to reach 8.4 billion by 2024 – more than the world population.
What is a Voice Assistant?
A voice assistant is an application or program that uses voice recognition technology and natural language processing to recognize human speech, translate words, accurately respond, and perform the desired actions. Voice assistants have dramatically transformed how customers search and give online commands. In addition, voice assistant technology has turned our everyday devices such as smartphones, speakers, and wearables into intelligent applications.
Points to keep in mind while interacting with digital assistants
The purpose of voice assistants is to make it easier for you to interact with your device and evoke the appropriate response. However, when this doesn’t happen, it can get frustrating.
Having a one-sided conversation is no fun, and before it can turn into a shouting match with an unresponsive application, here are some things you can do.
Keep it down and give it time
Watching your tone gets the work done – even when interacting with artificial intelligence-powered voice assistants. Instead of screaming at, say, Google Home when it doesn’t respond, try talking in a neutral tone. Then, allow time for the machine to process your commands.
Create profiles for regular users
You can make the voice assistant smarter by creating profiles for those who regularly use it, such as your family members. Amazon Alexa, for instance, can recognize the voice of up to 6 people.
Keep the requests simple
Your voice assistant, like Google Assistant, might be working on advanced technology, but it certainly can’t be expected to keep up an almost-human-like conversation. When the voice assistant is unable to comprehend context, it generally won’t be able to come up with an accurate response.
Be willing to clarify requests
Yes, if you can elicit a response at the first go, be ready to repeat or respond to clarify. Try rewording, simplifying, or rephrasing your questions.
How are voice Assistants (VAs) trained?
Before undertaking any of these processes, gathering extensive information about the project and its specific requirements is crucial.
Requirement gathering
To enable an almost human-like comprehension and interaction, the ASR has to be fed large quantities of speech data that caters to the specific project requirements. In addition, different voice assistants perform different tasks, and each needs a specific type of training.
For example, a smart home speaker such as Amazon Echo designed to recognize and respond to instructions has to discern voices from other sounds such as blenders, vacuum cleaners, lawn mowers, and more. Therefore, the model must be trained on speech data simulated under a similar environment.
Speech collection
Speech collection is essential as the voice assistant should be trained on data related to the industry and business it serves. In addition, the speech data should have examples of relevant scenarios and customer intent to ensure that the commands and complaints are easily understood.
To develop a high-quality voice assistant catering to your customers, you would want to train the model on speech samples of the people representing your customers. The type of speech data you procure should be similar linguistically and demographically to your target group.
You should consider,
- Age
- Country
- Gender
- Language
Types of Speech Data
Different speech data types can be used based on the project requirements and specifications. Some of the speech data examples include
Scripted Speech
Speech data containing pre-written and scripted questions or phrases are used to train an automatic interactive voice response system. Examples of pre-scripted speech data include, ‘What is my current bank balance?’ or ‘When is the next due date for my credit card payment?’Dialogue Speech
While developing a voice assistant for a customer service application, training the model on a dialogue or conversation between a customer and a business is essential. Companies use their call database of real-call recordings to train the models. If call recordings are unavailable or in case of new product launches, call recordings in a simulated environment can be used to train the model.Spontaneous or unscripted speech
Not all customers use the scripted format of questions to their voice assistants. That’s why specific voice applications need to be trained on spontaneous speech data in which the speaker uses their utterances to converse.Unfortunately, there is more speech variance and diversity of language, and training a model on identifying spontaneous speech requires massive quantities of data. Yet, when technology remembers and adapts, it creates an enhanced voice-powered solution.
Transcription and validation of speech data
After a variety of speech data is collected, it has to be accurately transcribed. The accuracy of the model training depends on the meticulousness of the transcription. Once the first round of transcription is done, it has to be validated by another group of transcription experts. The transcription should include pauses, repetitions, and misspelled words.
Annotation
After the transcription of data, it is time for annotation and tagging.
Semantic Annotation
Once the speech data has been transcribed and validated; it has to be annotated. Based on the voice assistant use case, categories should be defined depending on the scenarios it might have to support. Each phrase of the transcribed data will be labeled under a category based on meaning and intent.
Named Entity Recognition
Being a data preprocessing step, named entity recognition involves recognizing essential information from the transcribed text and classifying them into predefined categories.
NER uses natural language processing to undertake NER by first identifying entities in the text and putting these into various categories. The entities could be anything that is constantly being discussed or referred to in the text. For example, it could be a person, place, organization, or expression.
Humanizing Artificial Intelligence
Voice assistants have become integral to our everyday lives. The reason for this phenomenal increase in adoption is they are offering a seamless customer experience at every stage of the sales journey. A customer demands an intuitive and understanding robot, and a business thrives on an application that doesn’t tarnish its image on the internet.
The only possibility of achieving this would be to humanize an AI-powered voice assistant. However, it is challenging to train a machine to understand human speech. However, the only solution is to procure a variety of speech databases and annotate them to detect human emotions accurately, speech nuances, and sentiment.
Assisting businesses in developing a high-end voice assistant for various needs is Shaip – the sought-after annotation service provider. Choosing someone with experience and a solid knowledge base is always better. Shaip has years of dedicated experience catering to various industries to enhance their intelligent assistant capabilities. Reach out to us to know how we can improve your voice assistant competencies.
[Also Read: The Complete Guide to Conversational AI]
