Audio data collection for agentic AI focuses on capturing messy, context-rich speech data that fine-tunes existing ASR models rather than building new ones from scratch. In 2026, high-resource languages like English have achieved baseline parsability, meaning the traditional focus on demographic coverage (female vs male, old vs young, city vs country) has shifted. Instead, the emphasis is now on niche demographic groups, domain-specific vocabularies, and real-world acoustic conditions that challenge even the best automatic speech recognition systems.
Agentic AI and new open-source multilingual base models are about to change the audio data requirements for ASR tools, but not completely. The need for high-quality, purpose-built audio data collection remains essential for systems operating in real-world conditions.
The Current State of ASR: What Works and What Doesn’t
It’s 2026. If you speak English, even if it’s not American or British English, Automatic Speech Recognition works on your voice, and automatic transcriptions are accurate enough that you can figure out what an unfamiliar person in your meeting said even if the transcription got it wrong. All of your meetings have live captions, and you have to admit that sometimes the bot catches what someone says even when you don’t.
But ASR and automatic transcriptions still don’t catch it all, and they still get it wrong in ways that people do not.
Many of the errors that remain in automatic transcriptions come about because the tool doing the transcription does not attend to the greater context, something that humans find it difficult to NOT attend to. An Agent monitoring a transcription can provide this context, allowing the transcription tool to select the most likely spelling, not just based on the syntax (or grammar) of the immediately preceding words, but from the entire context of the conversation, which is not just the conversation itself.
Real-Life Examples of ASR Problems in English
Humans using the company-specific acronym ‘LDP’ know what it refers to. With a voice-onset time misalignment, the ‘D’ may sound like ‘T’ or ‘C’, and the ‘P’ may sound like ‘B’. Other glitches in the sound can cause the ‘D’ to be heard as ‘P’ or the ‘P’ as ‘C’. The following are actual errors transcribed by an otherwise excellent ASR, none of which were called out as problematic by the participants in the conversation, because every person in the call knew what to expect the acronym to refer to: LTP, LCP, LDB, LPC.
Correctly spelling someone’s name requires knowledge of who the actual referent is. If that person is logged into the conversation, then, if an Agent can confirm that the referent is part of the conversation, they can also spell the name correctly, and not vacillate between spellings. For the Tanias and Tanyas in the world, this is an important distinction! (If you’ve never met a Tania or Tanya before, then you can take my word for it that the I or Y distinction really does matter to us.)
Other common ASR challenges include different accents from different speakers, or the same speaker changing their accent (Halo vs Hell-o!), and distinguishing filled pauses from slow speech (Uh vs a vs ah).
The Shifting Rules of Audio Data Collection
Collecting audio data for ASR in 2026 is not playing by the same rules as it was 10 or even 5 years ago. Now, the focus is on messy data that humans can still make sense of easily. Demographic coverage is no longer about ensuring the main groups of female vs male, old vs young, city vs country speakers are included, because, for English and other high-resource languages, this baseline level of parsability has largely been achieved.
A notable milestone was recently achieved for ASR, with Meta releasing Omnilingual (available on GitHub), giving the world ASR in 1,200 languages at below 10% character error rates (CER). That’s a significant percentage of the estimated 7,000 extant languages used in the world today.
Do You Still Need Audio Data Collection?
The immediate question that springs to mind is: Do I even need to do any audio data collection for my company’s ASR tool? And the answer is, even more than ever, it depends.
As the Fun-ASR Technical Report from December 2025 notes: ‘Experimental results show that while most LLM-based ASR systems achieve strong performance on open-source benchmarks, they often underperform on real industry evaluation sets.’
If the users of an ASR voice assistant speak clear English, even if it’s accented, and if they rarely use specialised language, then going with an off-the-shelf solution is the simplest and cheapest option. But if your system needs to operate in real-world conditions, handling specialised or domain-specific vocabularies, mixed accents, code-switching, or extended, context-rich interactions, then high-quality, purpose-built audio data collection remains essential.
The Challenge of Conversational Speech
Research from Linke et al. published in August 2025 demonstrates the gap between controlled and natural speech: ‘ASR for dialects and non-dominant varieties of a well-resourced language [such as German]… performs acceptably for Austrian German read speech (11.8% WER), but not for speech from casual conversations among closely-related persons (41.8% WER).’
Obtaining decent demographic coverage of languages with good baseline ASR models means that audio data collections now need to include speakers with a range of ethnic backgrounds and accents, as well as a range of tone and sentiment. This data is far more ‘messy’ than the data of old, but its purpose is to fine-tune an existing model, rather than establishing a base model. Targeting these niche demographic groups improves performance in a way that collecting more of the same data does not.
The same research confirms: ‘Systems fine-tuned with data from the same variety and speaking style [as the target users] require less context and perform overall better than zero-shot systems.’
The Foundation of High-Quality Data
While some things change, some stay the same. Underlying all of the best results in ASR today is high quality, specialised data, and lots of it.
- A recent speech-to-speech model with excellent ethical reasoning and safety alignment, with coverage of 200 languages, achieved its outstanding results by beginning with 400,000 hours of conversational and ‘expressive’ speech.
- The final pre-training dataset for Omnilingual contained over four million hours of speech data.
- Google’s Universal Speech Model was pre-trained on over twelve millions hours of speech data.
- Microsoft says only 40 hours are needed to fine-tune a professional voice in a high-resource language.
What This Means for Your ASR Strategy
Agentic AI does not eliminate the need for audio data collection, but it does change what kind of data matters. Performance gains increasingly come from fine-tuning models on specific demographics and domain-specific interactions.
In 2026, successful ASR systems are built not just on strong base models, but on high-quality audio data with specific use cases in mind.
