Most teams discover the hard way that audio datasets are either their biggest asset or their biggest bottleneck. You can have a state-of-the-art architecture, but if your speech data is narrow, noisy, or poorly labeled, your model will struggle in production (WER often spiking 20-40% on accents/noise mismatches). In contrast, even modest models can perform remarkably well when trained on carefully designed, diverse, and well-governed audio datasets—like those cutting call resolutions 35% in contact centers.
Success comes from data-centric practices—defining objectives, engineering diversity, enforcing quality, and treating metadata and governance as first-class concerns. In this article, we'll share how we apply these ideas at Andovar across our multilingual voice data collection, custom speech data, and multilingual data annotation projects.
This article is part of our broader speech data strategy playbook.
A “better” dataset is not just a bigger folder of WAVs. Audio‑dataset best‑practice and multilingual‑dataset guides consistently highlight a set of characteristics:
The best corpora are those that are “high‑quality, diverse, and well‑annotated,” with clear structure and documentation, not just large.
That’s the bar we aim for when we design datasets for clients.
You should first start with objectives, not microphones.
Before any recording, we typically walk clients through:
Once this is completed, we define dataset objectives and target languages, design your data structure and metadata, then choose collection methods (crowdsourcing, studios, field recordings) that align with those goals.
At Andovar, we turn this into a concrete plan: which off‑the‑shelf datasets can provide a baseline, where custom speech data is needed, and how to enforce diversity and governance from day one.
Need help translating your voice roadmap into a data plan?
We can map your use cases, markets, and risk profile into a concrete speech data strategy—combining off‑the‑shelf datasets with targeted custom speech data and multilingual voice collection.
Design your dataset strategy
Diversity is a performance feature, not a nice‑to‑have. Under‑represent accents, dialects, or demographics, and you get predictable performance gaps.
Things we focus on, in line with external recommendations:
This is known as “auditory diversity”: accounting for regional accents, dialects, and speech patterns to ensure models perform across geographies and demographic groups.
By using our multilingual voice data collection services and global contributor network, we can recruit speakers in major and low‑resource languages, and design prompts that reflect real‑world usage instead of just lab scenarios.
Quality isn’t glamorous, but it’s critical. Quality over quantity is key: clean, consistent recordings and labels beat massive noisy corpora.
Key practices we advocate:
High‑quality preparation directly impacts the reliability, scalability, and ethical standing of your ASR systems.
We build these gates into our custom speech data workflows so you get consistent input for both training and evaluation.
Want your dataset to be high‑quality, not just high‑volume?
We can design an end‑to‑end process—collection, preprocessing, annotation, and QC—that keeps your audio datasets clean, diverse, and ready for production training.
Talk to us about dataset quality
Audio‑dataset data governance—policies, access controls, and documentation—is essential for quality and compliance. That’s doubly true for speech, where privacy and right sare front‑and‑center.
Practical steps we recommend:
We implement this by:
Project Overview
Andovar built a 90,000+ sample multilingual audio dataset for a leading e-commerce platform's voice shopping AI, targeting English, Mandarin, Spanish, French, and Hindi markets. This custom dataset powered seamless product search, recommendations, and checkout via voice across mobile and smart speaker devices.
Traits Applied
Results and Impact
The voice assistant hit 93% query accuracy across languages (up 22% from off-the-shelf data), drove 18% higher conversion rates, and scaled to 10M+ users without ethical flags or bias complaints.
There’s no one‑size‑fits‑all number. Guides to audio datasets and multilingual corpora emphasise that quality and coverage matter more than raw hours: a smaller, well‑targeted dataset often beats a much bigger but noisy or biased one.
Most practical advice suggests a hybrid approach: start with suitable openor off‑the‑shelf datasets where licensing and coverage are appropriate, then add custom speech data to cover your domains, languages, and risk areas. That’s also how we work at Andovar.
Use metadata to track languages, accents, age bands, gender, and environments, then set explicit targets based on your user base. Several multilingual dataset guides recommend treating this as a design goal, not an afterthought.
Preprocessing—format standardisation, noise reduction where appropriate, segmentation—is essential to make large audio collections usable and to avoid training on inconsistent or unusable data. Done right, it reduces model errors and speeds up labeling.
We can audit your existing corpora, improve annotation and metadata via our multilingual data annotation services, and design focused custom speech data projects to fill diversity or domain gaps. We can alsohelp you apply governance and quality gates so future data arrives in acleaner, more usable state.
About the Author: Steven Bussey
A Fusion of Expertise and Passion: Born and raised in the UK, Steven has spent the past 24 years immersing himself in the vibrant culture of Bangkok. As a marketing specialist with a focus on language services, translation, localization, and multilingual AI data training, Steven brings a unique blend of skills and insights to the table. His expertise extends to marketing tech stacks, digital marketing strategy, and email marketing, positioning him as a versatile and forward-thinking professional in his field....More