Today, most advanced messengers, personal assistants, and voice apps use some level of natural language processing (NLP) to power their linguistic interfaces. For NLP to be successful, computers must learn and understand human language the same way we do. Advancements in NLP for linguistics applications are driven by machine learning (ML) algorithms that are based on ground truth data – data that is collected from real-world scenarios capturing accents, international dialects, speech patterns, cadence, and other speech distinctions and behaviors.
The practice of collecting speech data is the foundation on which (ML) algorithms are designed and refined. This linguistics data is key to speech-driven machine learning programs that utilize NLP. Lacking sensitivity or failure to understand the surrounding nuances of language can lead to misinterpretation of the core data and compromise the program.
Q Analysts has learned, through many years of experience, what common challenges and best practices are associated with speech data collection to gather the highest level of data quality.
How much is enough: While you can’t scan seven billion people around the world for practicality’s sake, you do need enough data to ensure that the algorithms work correctly. Human language, with different dialects, accents, tones, and pitches, can be confusing and difficult for computers to learn. The number of participants is determined by the algorithm – a larger number, usually in the 10,000 range, is common early in the process, but as the algorithm evolves, fewer are needed.
Speech data capture is not easy: The most critical step is to fully understand what kind of speech data is needed, along with all the associated parameters, before collecting the data. If 2,000 participants are needed for a linguistics application, deciding how and where to find those people with varying accents, tones, and cadences in their speech will require planning and research. Failing to work through this process in a methodical way will only lead to extra cycles, time, and money to get the right data.
There is no standard: A common misconception is that there is, or should be, a standard for data capture. However, this manner of thinking does not take into account that each project is unique, based on the product and the particular scenarios needed for optimizing it. You might standardize the execution stage, but not when planning and designing the data capture process, which requires innovation, creativity, and research. Experience plays a crucial role, for example having a knowledge of demographics and where to find them. Expertise in accents, dialects, speech patterns, cadence, and other speech nuances and behaviors will more effectively guide the data collection process for the best results.
The most critical stage of a speech data collection project is at the beginning. It is important to make a full evaluation at the onset and ask as many questions as possible. Identify demographics, location, and the right number of participants, obtaining information related to accents, tones, dialects, and other speech patterns. Having a thorough understanding of these requirements will help effectively determine what the optimal parameters will be for capturing the highest-quality, best-fit speech data. For more, read our Case Study: on Natural Speech Data Collection.