About the Client
The client is a US-based AI research company that builds off-the-shelf (OTS) training datasets for Voice AI developers. Their product: production-ready conversational audio corpora that AI companies license to build or fine-tune speech models without collecting raw data from scratch. Rather than standing up a custom data collection program for every product, a Voice AI developer can license a ready-made dataset from the company and go straight to model training.
Their current 2,500-hour program targets two Voice AI model types: call center (CC) and general conversation (GC), across nine major Indic languages: Hindi, Bengali, Gujarati, Marathi, Telugu, Tamil, Malayalam, Kannada, and Urdu. Most commercially available training data skews heavily toward English, and the company is building the annotated corpus that Indic language audio annotation for Voice AI has been missing — production-quality, conversational audio across nine languages.
The Challenge
What Is Off-the-Shelf Voice AI Training Data?
OTS training data is pre-built, annotated audio that AI developers license rather than collect and label themselves. For Voice AI specifically, it includes transcribed speech, speaker labels, timestamps, and background noise tags: everything a model needs to learn from real conversation without standing up a data program from scratch. The quality bar is high because the data will train models sold to multiple end-users. A single consistency issue doesn't just affect one model. It propagates across every product built on that dataset.
The Problem
The baseline for most Indic language speech models is 50-60%, a gap driven by a shortage of annotated conversational data and inconsistent annotation quality across language pairs. Conversational audio is harder to annotate than scripted speech: speakers overlap, switch languages mid-sentence, use fillers, and speak in regional variants that require native-language judgment to capture correctly.
What Didn't Work
Before Taskmonk, the client used Dataloop. Three operational failures made it unworkable at scale.
No visibility on reporting. There was no reliable way to track annotation progress, quality metrics, or batch status in real time. Managing a 2,500-hour, nine-language program without project-level reporting isn't a minor inconvenience. It makes it impossible to manage SLAs, catch quality drift early, or plan throughput.
Extreme slowness loading tasks. Audio files loaded slowly inside the annotation interface. For annotators working through hours of conversational clips, load-time bottlenecks directly cut throughput and make it harder to maintain focus and consistency across a session.
An unresponsive support team. When operational issues surfaced, they weren't resolved. For a program of this scale and complexity, that compounds fast.
The client needed a platform with a managed workforce of native-language annotators, operational transparency, and a support team that showed up.
The Solution
Taskmonk deployed a managed audio annotation workforce and platform built for multilingual speech data. Taskmonk built the workflow around the project's core requirements: accurate transcription across nine Indic languages, a verbatim spec with no room for interpretation drift, and operational transparency throughout.
ElevenLabs assisted in building the annotation workflow, serving as a preprocessor for the raw conversational audio ahead of transcription.
The annotation spec for this project was demanding. Every clip needed verbatim transcription in the native script for each language: no grammatical corrections, no standardization, exactly as spoken. On top of that: turn-by-turn speaker diarization with consistent speaker IDs, timestamp segmentation for every speaker turn, and background noise tagging using a defined set of tags. Getting nine languages to the same standard simultaneously, with that level of annotation detail, meant native speaker annotators weren't a nice-to-have. They were the only option.
Verbatim Transcription with Custom Workflow Configuration
Taskmonk configured the annotation workflow to meet the project's complex multilingual requirements. Custom configurations ensured that filler words, false starts, incomplete words, overlapping speech, code-switching, and background events were captured and handled consistently. These configurations were standardized across nine parallel language streams, enabling uniform annotation quality and consistent edge-case handling throughout the project lifecycle.
Pro tip: Verbatim and clean-read transcription aren't interchangeable. ASR and Voice AI training almost always needs verbatim: every filler, false start, and overlap captured as spoken. Lock this down in the spec before the first batch ships, not after.
Speaker Diarization and Timestamping
Each conversational clip required turn-by-turn speaker diarization with consistent speaker IDs across the clip, plus start/end timestamp segmentation for every turn. Annotators tagged background noise events using a defined taxonomy.
The Results
The client's target was 90%+ transcription accuracy across all nine languages. Taskmonk delivered.
- 1,100 hours annotated and delivered across 9 Indic languages, out of a 2,500- hour program scope
- 90-95% transcription accuracy achieved across all nine languages, up from a 50-60% baseline
- Multiple model use cases supported: Call Center (CC) and General Conversation (GC) datasets annotated under a unified specification, ensuring consistent training data quality across distinct speech domains
- RTL transcription supported for languages such as Urdu, enabling native-script annotation, review, and quality control while maintaining consistent workflows across both left-to-right and right-to-left languages
- Real-time project dashboard replacing blind progress tracking from Dataloop
- Full speaker diarization and timestamping delivered across all languages with consistent speaker ID conventions
Why Taskmonk for Indic Language Audio Annotation
Most annotation platforms offer audio transcription as a feature. Getting to 90%+ across nine Indic languages simultaneously is an operational problem, not a tooling one. It needs a workflow configured for the edge cases of each language, annotators who understand the scripts and speech patterns, and reporting that surfaces problems before they compound.
Custom workflow configuration. Taskmonk configured the annotation pipeline to handle the complexity of nine parallel language streams under a single, standardized spec. Filler words, false starts, code-switching, and background events — all handled consistently, across all nine languages, from the first batch to the last.
RTL and multi-script support. Languages like Urdu require right-to-left annotation, review, and QC workflows. Taskmonk supported native-script annotation across all nine Indic languages, including RTL, without creating separate pipelines or consistency gaps between language groups.
Managed program ownership. The data annotation services layer means the client has one accountable owner for the full program: workforce, guidelines, QC, delivery, and the support team that answers when something needs fixing.
Taskmonk at a Glance
Taskmonk has processed 480M+ tasks across 6M+ labeling hours, with 24,000+ annotators and a 4.6/5 rating on G2. If you're building Voice AI training data across any language, book a demo to see how the platform handles your audio before signing a contract.
What's Next
The company is continuing to annotate the remaining 1,400 hours of the current 2,500- hour scope with Taskmonk.
Nine languages, two model types, a 40-45% accuracy lift. That's what audio annotation at scale looks like when the workflow, the spec, and the QC are all built for the same problem.
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