Crowdsourced voice recordings and their relevance for the development of speech recognition systems
Crowdsourced voice recordings have evolved to play a critical role in the development of speech-controlled apps. As speech recognition rapidly grows from a novelty to a daily necessity, you can expect the demand for both voice recordings and voice-activated systems to rise concurrently.
According to Grand View Research, the demand for voice-activated systems and devices is expected to be worth approximately $32 billion by 2025. But what exactly are voice recordings used for? Why are crowdsourced voice recordings important?
The Role of Voice Recordings in Voice-Activated Systems
The future of human-machine interactions depends on voice control. Whether it’s voice assistants, telephone systems listening for commands, or voice-activated Internet of Things (IoT), voice recordings help train intelligent algorithms and enable speech recognition.
Virtual agents already play a crucial role in banking, call centers, telehealth, and infotainment systems in automobiles. These are all only possible because of speech recognition systems. However, most don’t really deliver the best end-user experiences.
In a post-pandemic world, we can expect to see many more human-machine, contactless interactions in the workplace, home, and retail. As a result, the industry has a growing need for vast amounts of voice data to build reliable and comprehensive services.
For example, these speech recognition systems must recognize accents, dialects, protect against fraud and impersonation, and even identify the user’s emotional state and respond appropriately (especially in healthcare). In this scenario, template answers like “I’m sorry, I do not understand, could you repeat that,” just don’t cut it.
What is the Role of Crowdsourced Voice Recordings in Voice-Activated Systems?
Early incarnations of speech recognition technologies were pretty clunky because of gender and racial bias. For example, if you had an accent, the artificial intelligence (AI) that powered the product wouldn’t understand what you’re asking it to do.
To enable successful human-machine interactions, we need to build an all-inclusive speech recognition system. The best approach here is to adopt crowdsourced voice recordings to expose and teach machine learning (ML) algorithms to recognize different accents, dialects, and phonation types.
Phonation types essentially describe the different ways we produce sound through the vibration of our vocal cords. There are two broad categories of phonation types, namely, modal and nonmodal.
Modal phonation describes how vocal folds make complete contact during the closed phase of the phonatory cycle. Nonmodal phonation (of course) is the opposite of that. For example, breathy and creaky voices are a form of nonmodal phonation.
Why is Crowdsourcing Important to Speech Recognition Systems?
Crowdsourcing different types of voice recordings is the first step to build an all-inclusive voice AI. This method exposes ML algorithms to different tones, genders, accents, and dialects. Over time, smart algorithms learn from these extensive data sets to better understand and respond to users’ (or customers’) questions.
Repeating questions when engaging with an automated system is frustrating and could potentially lead to abandonment. With crowdsourced voice recordings covering a wide range of accents, modal and nonmodal phonation, genders, and more, you can negate these types of situations. This approach goes a long way to deliver enhanced customer experiences by providing near-human-like conversations.
These almost human-like interactions encourage more engagement. ML algorithms will continue to train based on the data sourced and structured in workflows, including collecting, annotating, transcribing, and tagging voice recordings. Through different stages of validation, this technology will only keep getting better, ensuring accuracy and saliency.
A voice assistant that learned from crowdsourced voice recordings will understand what you say (the first time you say it) even if you’re not a native speaker. It works by collecting the speech data, transcribing it to text, validating it, and then annotating it to derive greater value from the data – for example, to help the voice assistant understand the user’s intent.
AI will match the question or instruction with the appropriate response, and the dialogue will continue in this manner. Human-machine engagements where the speaker was pleased with the outcome are added to the voice dataset (and the intelligent algorithms will get better by learning from it).
So how do you crowdsource such a diverse database of voice recordings?
In this scenario, you can run a campaign to collect and build an extensive database yourself, or you can engage a third-party party provider who has already done that. The former is a lot more time and resource-intensive. You’ll have to recruit and record different types of voices from across the planet to build your voice database from scratch.
If you hire Clickworker to crowdsource the creation of audio datasets, you’ll get immediate access to more than 2.2 million clickworkers around the world to record, transcribe, and classify voice recordings in over 30 different languages and numerous dialects.
For example, you have the option of building a database of voice recordings based on your specific industry, target audience, and so on. Furthermore, each voice recording is recorded according to sentences provided in writing (text to speech).
These crowdworkers or clickworkers could also record various other expressions to provide the algorithms with different variations of the same sentence to get a sense of the goal of the sentence (which remains the same).
Once evaluated, these recordings are updated accordingly. This speech recognition training data is critical as no two recordings will ever be the same. This approach enables a more accurate representation of the local and international target audience.
You can also leverage existing crowdsourced speech datasets to test your voice recognition application, build a proof of concept, and engage in everyday tasks and voice-enabled interactions.
Enables inclusivity and better market representation
Delivers enhanced customer experiences
Negates the need to repeat questions
It helps build brand value and loyalty
Sets the stage for seamless human-machine engagement
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