Q Blog

Data Collection for Natural Language Processing

Enterprise investments in artificial intelligence (AI) are on the rise. Two-thirds of companies say they’ve accelerated AI adoption plans, and nearly 90% agree that AI is quickly becoming a mainstream technology. Many of the mission-critical AI solutions used by today’s enterprises—including personal assistants and voice apps—rely on natural language processing (NLP).

NLP aims to build computing systems that understand speech and human language and respond with speech or text much as humans do. For NLP to succeed, computers need sophisticated machine learning (ML) algorithms based on ground truth data—authentic human speech collected from real-world scenarios. An accurate data set must include a cross-section of accents, international dialects, and cadences, along with other speech distinctions and behavior.

Gathering linguistic data is the foundation of ML algorithms that use NLP. If the core data does not represent the nuances of language, the resulting ML program can be faulty. However, following proven best practices can set organizations up for successful speech data collection and ensure the highest level of data quality.

Best Practices for Speech Data Capture Success
Speech data capture is not easy. No two NLP data capture projects are alike and finding the right mix of participants is a frequent challenge. Common roadblocks can be readily avoided by understanding what to expect from a speech data capture project.

Know How Much is Enough
There is simply no way to collect speech data from all seven billion people around the globe to understand every nuance of human language. Instead, building effective NLP algorithms means strategizing to collect different dialects, accents, tones, and pitches to help computers learn the variations of human speech patterns. Early in the process, data might be required from as many as 10,000 participants, but that number decreases over time as the algorithm evolves.

Create a Solid Data Capture Plan
Effective speech data collection requires planning to be successful. Before sourcing those first few thousands of participants, companies need to know how and where to find people with varying accents, tones, and cadences in their speech. In-depth research and planning are a must to get the right data and avoid wasted expense and time.

Recognize that Each Project Is Unique
It seems that there should be a standard for speech data capture—but that’s not the case. The reason: each data capture project is unique, based on the end product and how to optimize it. While it’s possible to standardize at the execution phase, planning and designing data capture requires innovation, flexibility, and research to produce results.

Experience matters. Data capture experts can coach enterprises on where to find participants with representative accents, dialects, speech patterns, and other speech nuances and behaviors. In addition, experts can craft enrollment strategies— including marketing outreach and incentive programs—to attract participants and motivate them to show up for speech data collection sessions. With these approaches, enterprises can build a successful data collection process that forms the foundation for an effective ML algorithm.

Start with a Strong Foundation
Recent research has found that companies are investing more in NLP, with the global NLP market projected to reach $35.1 billion by 2026. This rapid growth signals good news for the many enterprises that recognize the power and potential of using AI to understand human language.

At the start of any NLP project, a company must make a complete evaluation and ask as many questions as possible to uncover their data collection requirements. They must identify participant numbers, demographics, and locations. Additionally, they must know what data they need related to accents, tones, dialects, and other speech patterns.

Clarifying these requirements will help establish the right process for capturing the highest-quality, best-fit speech data. For a real-world example of how this works, read our case study: on Natural Speech Data Collection.

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