Machine learning and artificial intelligence (AI) software is increasingly capable of understanding human communication, empowering businesses and people alike with everyday speech-driven solutions. At the heart of their ability to understand sits the process of speech data collection.
If you’re wondering what speech data collection entails and how it works, we’re going to share what you need to know in this glossary article.
What is Speech Data Collection?
Speech data collection is how machine learning programs learn and can apply natural language processing (NLP) to understand humans’ speech. While it may sound simple enough for a microphone to listen to all that is being said, the most important aspect of speech data collection is accuracy. With varied accents, dialects, speech patterns, tones, and background noise, speech data collection has its fair share of challenges to overcome in order to work at an optimal level.
Why is Speech Data Collection important?
Speech data collection is integral to the usability of speech-data software and technologies. There are various goals that can be accomplished with this kind of data, including:
Training AI Models
As alluded to above, speech data collection is necessary to train artificial intelligence systems that are behind speech recognition software, voice assistants, and voice search. The data collected gets analyzed so that the AI model can learn the distinctions in spoken language, such as specific phrases, acronyms, accents, and tones. The greater the dataset, the more the AI has to learn from.
Speech Recognition Improvement
Speech recognition software is directly affected by the quality and accessibility of available data. With robust datasets, models are able to predict speech patterns and understand them better, which leads to better outputs. Ultimately, the greater the dataset, the more the AI has to learn from.
Applications
Virtual assistants, speech AI, language translation tools, and customer service bots are just a few examples of the tools that require speech data collection in order to function properly.
Types of Speech Data
Speech comes naturally, so people often don’t have to pay too much attention to the words that they say or that are being said in order to communicate. However, when you think about all the words you may mumble in a day, you’ll notice that there are a lot of different types of speech and utterances that are made. From everyday vocabulary to well-known phrases, filler words, and even sounds, human speech is nuanced and varied.
For artificial intelligence and machine learning to function and mimic how a human brain works, the technology has to break down the different types of speech data to structure it for interpretation.
Here’s a look at some of the types of speech data that exist on the spectrum:
Conversational Speech
Conversational speech is the unscripted words that are exchanged between two or more speakers. For AI, there are some challenges that come along with conversational speech, namely the issue of context and also the fact that speakers can overlap when they talk. Since conversational data is unlimited in its constraints and unpredictable by nature, it can be hard to train on, and thus, requires a larger amount of data for the AI to learn from.
Scripted Speech
Scripted speech is the opposite conversational speech, as it is not naturally occurring, but rather prepared and read. Scripted speech is the most controlled form of speech data, which typically includes wake words and commands. Developers use this kind of speech data to train on how something is said, rather than what is being said. Researchers and developers will have chosen the most common speech commands for the technology in play, and then make sure that it will be able to understand different pronunciations of the same words, i.e. to accommodate accents and dialects.
Scenario-Based Speech
Scenario-based speech data adds in parameters, but still gives speakers some freedom in their choice of words. In this case, speakers are given a scenario and asked to come up with their own voice commands. Developers may request this type of speech data to get a natural sampling of how different people could ask for the same things. The result is variety- both in terms of what is being said, as well as how it is being said.
The best speech recognition software can understand all unstructured speech data and be able to transform it into accurate transcriptions for real-world applications. For example, aiOla’s speech AI is the first-of-its-kind to understand business-specific jargon, which means that anyone who speaks into aiOla, even using acronyms or nuanced vocabulary that is tailored to their business, will still be understood. In turn, aiOla transforms spoken words into fulfilled actions to streamline business processes and achieve operational efficiency. aiOla also understands any accent and language, and importantly, works in any acoustic environment, being unphased by background noises.
Key Components of Speech Data Collection
How and where does speech data collection take place? It can happen anywhere, but there are key components that are required, including:
- Audio recording equipment: A microphone must be present to capture the speakers’ audio.
- Participant recruitment: Humans are needed to do the speaking!
- Script preparation: If it is a scripted training, then a script must be prepared. Or if it’s scenario-based, the circumstance should be shared. Otherwise, conversational speech data can be collected with consent.
- Environment setup: Speech data collection can take place anywhere, but the setting will depend on the training set you wish to capture. You may record in a sound-proof room with no background noise or you may be in an environment that is specific to the background noise that’s naturally occurring for the speech recognition software’s intended use case.
- Data processing and annotation: Pre and post-process the data to get it ready for its annotation, which classifies components of audio with labels and “tags” for the NLP model.
Challenges in Speech Data Collection
Training AI is essential to improving its usability and accuracy, but it doesn’t come without its own hurdles, given the nature of speech data. A few notable things to consider are:
- Ensuring audio quality: Data quality is affected by distortion, such as poor recording conditions, as well as inconsistent speech patterns, which vary by speaker.
- Capturing diverse accents and dialects: All people speak in their own way, so accounting for accents, dialects, and demographic diversity is optimal to create a well-rounded dataset for training.
- Handling background noise: Background noise continuously pops up as a challenge because it’s hard to record somewhere without any interference.
- Addressing privacy concerns: A major concern tends to be privacy and ethics. For starters, you’ll need consent from participants to be recorded, with clear knowledge of how their speech data will be used. Additionally, it’s necessary to fully protect stored speech data from unauthorized access. In the case of sensitive and private information that is shared, data anonymization can be applied to protect individuals.
- Scaling collection efforts: To train AI models to their fullest potential and capacity, a large volume of data is best. Scaling data collection efforts calls for equipment, personnel, and storage.
Future Trends in Speech Data Collection
Technology continuously evolves. The process and method of speech data collection is also facing advancements, including:
Advanced AI-driven collection methods: Synthetic data generation can be helpful in instances where real data is limited.
Integration with other data types: Speech data is being combined with multimodal data, like body language and facial expressions to better interpret human communication.
Improved real-time processing: The speed at which speech data is being analyzed and interpreted is getting faster, which is great for real-world applications like customer service chatbots.
Closing Thoughts
Without speech data collection, there would be no speech recognition systems that could interpret and communicate with humans and like humans. Datasets of speech enable AI and machine learning algorithms to learn on their own and improve over time to better service the needs of the people who use them.
