Voice data is a huge asset in today’s super competitive world for speech recognition models. So, here’s how to get your hands on it.
Speech recognition is the backbone of the modern data-driven world. From smart cars to intelligent software processing documents, reliance on speech recognition is unavoidable and increasing.
Almost 50% of US citizens opt for voice search daily, generating tons of data. Research forecasts the speech recognition market to hit $27.6 billion by 2026.
And voice data, despite its challenges, is the fuel for speech recognition and voice generation applications. Natural human recordings train artificial intelligence (AI) models and chatbots.
In this post, then, we discuss the significance of voice data, sources for collection, and the benefits of extraction.
Types of Voice Data and the Sources
Speech recognition apps require data in the form of human audio for training. Depending on the nature of the project or the AI tools in development, it can be anything from simple statements to special instructions.
For instance, AI machine learning models need different categories of data to prepare a dataset, filter it, and then fine-tune it according to the requirements. Speech datasets can be broken down based on language, gender, dialect, and other attributes.
The data collection methods vary for each audio data type and application. You need to consider the requirements and specifications of many audio datasets in the project while gathering data.
Here are some of the most common types of voice data and their sources.
Custom Voice Collection
In terms of innovation, smart home devices have left everybody in awe. You can control smart home devices and appliances via voice over the internet. Particularly, smart home assistants that work on voice-based commands have become incredibly popular.
Custom voice data is used to train:
- Virtual assistants
- Voice bots
- Smart home devices and appliances
- Smart cars
- Speech recognition systems for security
Amazon’s Alexa is a voice-controlled virtual assistant that can manage smart home activities through audio commands. It can interpret your speech, understand the intent, and provide results by processing text into audio.
You can ask general questions from Alexa, get help with homework, prepare a to-do list, or request quick cooking conversions. For instance, “Alexa, what’s 10 times 100?”, “Alexa, add ‘visit the grocery store’ to my to-do list”, “Alexa, lock the back door”, or “Alexa, who was Einstein?”.
Athom Homey is capable of processing information in a multilingual setup. LG Roboking Vacuum Cleaner is another amazing smart device, which operates on voice commands, stops when it hears you clap your hands two times, and can detect the source of the sound too.
Similarly, HoneyWell’s Smart Thermostat functions with audio instructions. It has the ability to expand the command vocabulary based on its regular interactions. Moreover, with an intelligent shower controller, you can turn on the shower and set it to your preferred temperature.
Soundit is a wearable audio device with an extra boost of entertainment. It adds a soundtrack to whatever you are doing at the moment. It can also detect sound effects in a concert and amplify them for the user.
Similarly, Sleep.ai helps detect teeth grinding or snoring over a mobile phone app. All of this is done through collecting human data.
Source
So, to train and enhance AI/ML machines or models for speech recognition, the common data collection sources include:
- Human Speech Audio (Original words spoken by humans and greetings recorded in various languages, dialects, and accents)
- Digitally recorded sounds (human laughs, cries, coughs, screams, sneezes, or snores)
- Background tracks (sea waves, songs, alerts, rain, etc)
Call Center Voice Data
Customer reviews, sentiments, expectations, and experiences are a real treasure. The voice of your customers can tell you a lot about their experience with your product or services. It is also a great source of human speech datasets.
Companies that record phone calls can gather conversations, observe the findings, and drive insights for better decision-making, and improved customer experiences.
Today, call recording systems are completely digital and integrate seamlessly with the existing contact center, business telephony network, and cloud data storage. Some call center setups even come with screen and video capturing technology, in addition to spoken audio.
Companies that record phone calls can review the impact of refined policies and new strategies on the overall sales and customer experience. It can also help identify popular trends and highlight agents that need additional support and attention.
Additionally, some industrial companies are compelled to gather specific pieces of information for data protection and compliance reasons. Recording calls can significantly guarantee strict compliance with laws related to customer consent and notification.
Companies can also use the data for dispute resolution on issues like customer service standards.
Source
With increasing business needs and growing scope, it is hard to collect call center data and attend calls manually. Although speech-analysis technology has brought advancements in call centers, we have several other techniques to capture customer data.
Automating data collection and other repetitive processes can free up human agents to boost productivity and save time. Therefore, AI tools have become common for gathering and analyzing call center data.
Natural Language Processing (NLP), Machine Learning (ML), Speech-analytics, Conversational AI, and sentiment analysis are the fields that help call centers in automated data collection and analysis.
Off-the-Shelf Voice Data
Not every business has the benefit of specialized teams, trained data annotators, or advanced AI tools to collect data. Deployment and maintenance of AI-based models require frequent upgrades due to rapid advancements.
Considering these factors, a large chunk of the organizational sector prefers pre-built or off-the-shelf data.
In 2020, Gartner predicted that 35% of organizations would either sell or buy data shifting to off-the-shelf datasets. Third-party companies gather data and sell it to fulfill the requirements with limited risk and extreme convenience.
These prebuilt datasets are not just for organizations with limited budgets, but also for large-sized firms interested in scaling and improving ML-based models’ performance. Using off-the-shelf training data saves time and effort.
Off-the-shelf data can include text, images, audio, and video files. It is safer than internally gathered speech datasets because it is carefully collected and guaranteed to comply with privacy standards.
Source
Different third-party vendors offer pre-built or off-the-shelf datasets. For example, there’s the Summa Linguae collection of off-the-shelf speech, image, and video data sets.
Most data sets have a downloadable sample file to give you a preview of the capabilities of our ready-to-order or highly customizable data solutions.
Want to build your own voice data set?
Contact us now to learn how we can collect a custom data set for your unique AI solution.
If collecting speech dataset files yourself isn’t in your budget, off-the-shelf speech data sets have got your back.
These pre-built datasets can transform your data handling and analysis journey tremendously.
