Speech Transcription

This task turns talk into text—think “Call me later” typed out crisp or “What’s for dinner?” etched from a mumble (e.g., “Hi there” as words, “Uh, cool” with pauses)—to feed AI clean lines. Our team transcribes these voices, powering speech tech with spot-on scripts.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are pivotal in orchestrating the transcription and structuring of data for Speech Transcription within audio processing workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to produce transcribed datasets that enhance AI’s speech-to-text accuracy and usability.

Training and Onboarding

PMs design and implement training programs to ensure workers master speech-to-text conversion, punctuation accuracy, and context capture. For example, they might train teams to transcribe “Hey, slow down” with commas or “Yes” from a shout, guided by sample audio and transcription standards. Onboarding includes hands-on tasks like typing out clips, feedback loops, and calibration sessions to align outputs with AI speech goals. PMs also establish workflows, such as multi-pass reviews for tricky audio.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., transcribing 15,000 audio clips) and set metrics like word error rate, punctuation fidelity, or speaker clarity. They track progress via dashboards, address transcription errors, and refine methods based on worker insights or evolving ASR needs.

Collaboration with AI Teams

PMs connect transcribers with machine learning engineers, translating technical requirements (e.g., low error rates) into actionable transcription tasks. They also manage timelines, ensuring transcribed datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The transcribers, typists, or audio analysts perform the detailed work of converting and structuring speech datasets for AI training. Their efforts are auditory and textual, requiring keen listening and typing precision.

Labeling and Tagging

For transcription data, we might tag text as “full sentence” or “interjection.” In complex tasks, they label features like “hesitation” or “accented word.”

Contextual Analysis

Our team captures speech, turning “Can’t wait!” into text with flair or “Um, maybe” with pauses, ensuring AI reads the vibe right.

Flagging Violations

Workers review datasets, flagging misheard words (e.g., “cat” as “cap”) or garbled audio (e.g., noise overload), maintaining dataset quality and reliability.

Edge Case Resolution

We tackle complex cases—like slurred speech or overlapping voices—often requiring slow playback or escalation to transcription experts.

We can quickly adapt to and operate within our clients’ audio platforms, such as proprietary transcription tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of clips per shift, depending on the complexity of the speech and transcriptions.

Data Volumes Needed to Improve AI

The volume of transcribed speech data required to enhance AI systems varies based on the diversity of speech and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional ASR model might require 5,000–20,000 transcribed clips per category (e.g., 20,000 casual talks). For varied or noisy speech, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 clips per issue (e.g., misheard phrases) are often needed. For instance, refining a model might demand 5,000 new transcriptions.

Scale for Robustness

Large-scale applications (e.g., voice search platforms) require datasets in the hundreds of thousands to handle edge cases, rare accents, or ambient noise. A transcription effort might start with 100,000 clips, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky audio for further transcription. This reduces total volume but requires ongoing effort—perhaps 500–2,000 clips weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and textual precision across datasets.

Multilingual & Multicultural Speech Transcription

We can assist you with speech transcription across diverse linguistic and cultural landscapes.

Our team is equipped to transcribe and analyze speech data from global voices, ensuring accurate, culturally relevant datasets tailored to your specific AI objectives.

We work in the following languages: