Challenges in collecting diverse voice data

In the context of artificial intelligence (AI) permeating every aspect of life, diverse voice data collection is no longer an option but an urgent requirement. An AI system is only truly intelligent and useful when it can effectively understand and interact with all users, regardless of their region, language, or age group. Diversity in training data is the key for AI to overcome communication limitations, providing a fair, useful, and truly “community-integrated” experience.

However, the journey of diverse voice data collection is by no means easy. It demands serious investment in time, cost, specialized human resources, and a methodical, culturally and socially sensitive approach strategy. This article will delve into the main obstacles organizations often face, while also suggesting strategic solutions to help businesses gradually conquer these challenges, build high-quality, comprehensive voice datasets, and lay a solid foundation for future AI applications.

Major challenges in collecting diverse voice data

Approaching and mobilizing specific demographic groups

• Geographical and infrastructural barriers: Communities speaking minority languages or distinctive local accents often reside in remote, isolated, or island areas where travel conditions are difficult, and information technology infrastructure (internet, smart mobile devices) is still limited. This hinders both direct and online data collection from these communities.
• Lack of awareness and motivation to participate: Many people, especially older adults or those in rural areas, may not fully understand AI technology, the purpose of voice data collection, or see the direct benefits for themselves and their communities. This lack of information leads to hesitation, навіть відмову брати участь у проектах збору даних.
• Psychological and cultural barriers: Some communities have unwritten rules or cultural beliefs related to sharing personal voice and images. Shyness or self-consciousness about one’s voice (believing it’s not “standard” or “good”) is also a hindering factor. This requires the collection team to have a deep cultural understanding and a delicate approach.
• Limitations in the network of local collaborators: The absence of “bridges” – reputable individuals who understand local culture and language – to support communication, mobilization, and organization of collection efforts makes it difficult for projects to reach a large number of target participants.

Discrepancies in data quality and consistency

• Unevenness in recording equipment: In large-scale community collection projects, participants often use personal devices (mobile phones, tablets) with vastly different microphone quality and noise-cancelling capabilities. This leads to significant variations in the clarity, frequency, and volume levels of the audio files.
• Diverse recording environments: Background noise (traffic, conversations, household sounds, room echo, etc.) is an obstacle to collecting “clean” data. Participants recording themselves at home, work, or outdoors make controlling this factor extremely difficult, requiring complex post-processing noise filtering and review.
• Differences in pronunciation and script adherence: Even with a clear script, how each person reads, pauses, emphasizes, speaks at different speeds, and expresses emotions varies greatly. For languages with many pronunciation variations or no unified spelling standard, this problem becomes even more challenging.
• Difficulties in standardizing metadata: Collecting and managing accompanying information such as age, gender, region, language, and education level of the speaker accurately and consistently on a large scale is a challenge, yet crucial for later analysis and AI model training.

>> See more: How to ensure voice data quality for AI: From QA to QC

Linguistic, cultural barriers, and legal, ethical issues

• Challenges in translation and localization of materials: Collection guidelines, scripts, and consent forms need to be accurately translated into the participants’ native language and must also suit their writing style and cognitive level. A mechanical, unnatural translation can cause misunderstandings or reduce trust in the entity conducting the collection project.
• Ensuring informed consent: Participants must truly understand what data they are providing, for what purpose it will be used, who will have access, how long it will be stored, and what their rights are. Explaining technical and legal terms simply and understandably to diverse audiences is by no means easy.
• Respecting culture and avoiding sensitive content: Collection scripts should avoid topics or words that could be controversial, offensive, or inappropriate for the participating community’s culture or beliefs. This requires thorough research and consultation with local cultural experts.
• Compliance with data protection laws: Regulations such as GDPR (Europe), CCPA (California), and similar laws in many countries impose strict requirements on the collection, processing, and protection of personal data, including voice data. Businesses need to ensure their processes fully comply with these regulations to avoid legal risks.

Demands on cost, time, and project management resources

• Costs of recruiting and compensating participants: To attract a sufficient number of participants from minority groups or those with special requirements (e.g., voice with specific emotions), businesses often have to offer attractive compensation policies, significantly increasing the project budget.
• Costs for expert teams: Diverse voice data collection requires the involvement of many experts: linguists (to build scripts, check pronunciation), audio specialists (to set technical standards, handle post-processing), legal experts (to ensure regulatory compliance), and experienced project coordinators.
• Investment in technology and platforms: A robust platform is needed to manage the collection process, store data, annotate, review, and track progress. Building or renting these platforms also represents a significant expense.
• Extended implementation time: Compared to collecting data from a homogeneous group, approaching, persuading, guiding, and collecting from diverse groups often takes more time, requiring patience and flexible contingency planning.

>> You might be interested in: Popular voice data collection methods

Risks of data imbalance and lack of true representativeness

• “Majority rules” trend: Despite diversification efforts, data from easily accessible, large-population groups (e.g., urban youth speaking a standard dialect) often account for a much larger proportion than minority groups. This leads to “biased learning” in AI models.
• “Long-tail” Data: In reality, there is always a large number of voice variations that appear with very low frequency, which can be considered the “long tail” in a distribution graph. Collecting sufficient data for this entire “long tail” is an extremely significant challenge.
• Lack of representativeness in specific use cases: Even with data from many groups, if not collected in sufficient contexts, with diverse types of sentences, or varied emotions, AI models may still perform poorly in real-world situations they have not “learned” through.

What are the solutions to overcome the challenges?

Connecting with Communities and Collaborating with Local Organizations

Many international projects opt for collaboration with schools, non-profit organizations, and local community networks to reach potential user groups that are harder to access. Community-based data collection helps expand scale and increase representativeness thanks to diverse participants.

Establishing Flexible QA/QC Processes

Depending on the collection subjects, different guidelines, standards, and review methods can be applied (classified by age group, region, recording device). This helps optimize recording capabilities and improve data quality.

Combining Data Augmentation Technology

Using augmentation algorithms, converting accents, simulating noise, or supplementing with standardized recordings helps balance datasets among groups; this is a solution recommended by many large technology organizations.

Transparency Regarding Security and Benefits

Clear communication about the purpose of data use, security mechanisms, and a commitment not to misuse personal data will help increase participation rates in hesitant or vulnerable community groups. This transparency also builds long-term trust with the participating collection community.

Towards a fairer and more comprehensive AI future thanks to diverse voice data

Diverse voice data collection is not just a technical challenge but also a journey requiring perseverance, strategic vision, and ethical commitment. Difficulties ranging from community outreach, ensuring quality, managing costs, to balancing data are all problems needing intelligent and flexible solutions. However, with the continuous development of technology, close collaboration among stakeholders, and a human-centered approach, we can completely overcome these barriers.

BPO.MP, with extensive experience and proven capabilities through numerous large projects in data collection and processing, is proud to be a trusted partner for businesses on the journey to conquer diverse voice data. We provide comprehensive solutions, from building “tailor-made” collection strategies for specific requirements, deploying an extensive network of collaborators, applying advanced QA/QC technology, to ensuring the highest ethical and legal standards. By combining technological strength with a deep understanding of culture and language, BPO.MP is committed to accompanying you in building international-quality voice datasets, creating a solid foundation for the success of AI projects, and contributing to a future where technology truly belongs to everyone.