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TICTAG'S CASE STUDY

Audio Transcription Annotation

Applicable Industries and teams: Conversational AI , Audio AI, Digital AI Assistance, Speech Recognition, Law Firms

Problem at hand

Audio AI and Conversational AI teams often bank on automated solutions to provide them with transcriptions of their collected audio data. This might be a fast way to obtain the required data but does it do your Audio AI model any justice?

Teams working on Audio AI have identified the given shortcomings of an automated solution:

  • Limited Vocabulary & Accent Bias: These solutions are often NOT tailored to the niche requirement of your project.

  • Little immunity to background noise and low audio quality data: The solutions transcribe only what they can identify

  • Unrecognition of overlapping dialogue: Machines aren't able to accurately recognize the difference between  multiple voices. They simply consider it as one source and transcribe it accordingly. 

Tictag's Solution

Tictag leverages its crowdsourcing abilities and application of human intelligence to transcribe and cleanse a transcription of audio data:

  • Complete complex projects: our solution simplifies each job to ensure accuracy

  • x2 Speed: Our large crowd of taggers tackle this time-consuming, and challenging task and deliver it within the deadlines

  • The use of human intelligence to generate high-quality data: As we provide guidelines and sample images, Our taggers can take even the most complex use cases on polygonal data annotation and generate high-quality datasets

  • Quality Control to sieve out contradiction and inaccuracy in annotated data: Of course, we will not sacrifice quality over speed. We maintain both.

  • Respond faster to changes in requirements

Results

Tictag leverages its crowdsourcing abilities and on-the-go mobile platform to get you highly accurate transcribed data to for your Audio AI Models.

Don't compromise on what your AI can do. Blow your audiences away with an Audio AI that can be truly intelligent, using Tictag's best-in-class data preparation solution.

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