Many companies are using AI to create audio features for their customers. They have voice agents to provide customer service, speech-command features, voice accessibility options, and advanced conversational AI experiences to meet growing customer expectations. AI can also be used to improve efficiency. AI voice agents can handle simple questions or customer interactions, thereby reducing manual effort and/or freeing up employees to focus on more complex work. AI audio initiatives are not just a nice-to-have. As voice interfaces become a primary way customers interact with AI, organizations need systems that perform consistently across real-world environments, languages, and user populations. They’re becoming essential to staying competitive in today’s markets.

These initiatives are costly, though. Organizations may be starting them from scratch without much internal expertise or voice AI services. Executing audio-focused AI initiatives correctly is vital to ensuring measurable business value. Read how AI audio data services are helping organizations ensure their audio AI initiatives are high-performing for customers in every environment and use case.

Benefit #1 of AI Audio Data Services: Creating More Realistic Voices

AI voice agents, virtual assistants, and accessibility features aren’t useful if they sound robotic and clunky — or worse, are hard to understand. AI audio data services help companies by collecting and annotating the large volumes of high-quality audio data necessary for training high-performing voice AI systems. Providers of AI voice data services (such as Lionbridge) leverage global contributor networks and scalable platforms to quickly collect, annotate, and validate high-quality speech data. This enables organizations to accelerate AI development, deployment, and time-to-value. We also understand the complexities of developing voice AI systems that perform reliably across diverse speakers, languages, and environments. AI audio data services help AI systems perform reliably across scenarios involving:

• Background noise
• Accents
• Emotion
• Context
• Intent
• Volume variation
• Pitch variation

Our AI data services experts have worked on hundreds of AI voice projects. Here’s one example where our AI voice training solutions helped a company build an impactful, ROI-generating voice initiative.

Benefit #2 of AI Audio Data Services: Creating Effective Multilingual Voice Services/Features

Organizations expanding into new markets often rely on AI voice capabilities to provide localized, natural customer experiences. Training an AI voice agent to serve customers who speak new languages can be complicated, especially less common or indigenous languages. Beyond the typical vast amounts of data for each language, these projects require AI data solutions informed by deep linguistic and cultural knowledge. AI audio data services collect and annotate multilingual speech data that enables AI models to understand:

• Accents
• Dialects
• Slang
• Cultural norms
• Context

Working with AI and language solutions integrators like Lionbridge can help ensure that a voice feature deftly supports customers, patients, etc., regardless of the language they speak (and how they speak it). Lionbridge’s AI audio data services have assisted many similar initiatives. Here’s an example case study.

Benefit #3 of AI Audio Data Services: Augmenting Accessibility

Many companies and organizations want to ensure accessibility options for customers who are non-native speakers or have disabilities. Maybe they must meet compliance requirements or simply want to be more inclusive. Training speech recognition and conversational AI systems on comprehensive, high-quality datasets is necessary to successfully provide features like:

• Speech-to-text transcription
• Live captioning
• Audio descriptions for users who are blind or have low vision
• Expanded language access

Again, as noted above for other projects, these initiatives typically require large datasets that are procured quickly. Especially in these cases, human data, not synthetic data, will be most powerful. While synthetic data can accelerate development, human-generated data remains essential for capturing the variability and nuance of real-world conversations. Meeting these requirements often demands experienced AI audio data services providers.

Another key consideration is whether the data helps train the machine to handle the complexities of people in real-world environments and conversations. Data must reflect:

• Background noise
• Multiple speakers
• Emotional speech
• Speech impairments or differences
• Local and regional accents
• Industry-specific terminology

Gathering this kind of data ensures participants are fully supported with accurate, timely accessibility. When the AI system isn't adequately trained and evaluated, the result can be frustrating — maybe even more than if the patient, customer, etc. didn’t have any accessibility features offered. (Anybody who has watched a badly captioned movie understands this effect.) Poorly trained AI agents could also jeopardize compliance, resulting in fees, legal action, reputational damage, or worse. Choosing experienced, expert AI audio data services providers is vital for successful projects like this.

Benefit #4 of AI Audio Data Services: Eliminating Bias

Responsible AI is a hot topic among companies. It’s not just a nice-to-have; it’s also good business. Many younger customer demographics value companies that operate in a demonstrably ethical way. Additionally, creating AI agents that perform without bias ensures a positive customer/patient/participant experience. Lastly, eliminating biased AI agents helps companies avoid searing damage to their reputations. Having AI audio features that act with racism, misogyny, ableism, etc. is a surefire way to anger customers and garner bad reviews (online, in-person, and more). Reducing bias in AI systems requires diverse, representative datasets spanning different speakers, languages, cultures, and real-world scenarios. Organizations should partner with AI audio data services providers that offer globally diverse contributor networks and rigorous quality processes to reduce bias and improve model performance.