Challenges persist in conventional LLMs based on the data used to train them, the nuances of the source, or, at times, the lack thereof. Innovative solutions combining artificial intelligence with human expertise are emerging as game-changers in the industry. This is also true regarding the automated speech recognition (ASR) technologies required for transcription of documents. We have spoken about the ASR systems and our Human-in-the-Loop solution, but there are additional cases to be made to understand the depth of the situation.
The English-Centric Nature of ASR Development
Modern ASR systems are biased toward English speakers, particularly those with standard American or British accents. This bias stems from the historical development of these technologies, where training data predominantly consisted of English speech samples from specific demographic groups. Recent studies indicate that leading ASR platforms achieve error rates below 5% for standard English speakers. In comparison, these rates can soar to 20-40% for speakers of Indian English, African American Vernacular English (AAVE), or English spoken with various international accents.
Though challenges are evident with different accents of English, at times, we also need to be sure of the accuracy of specific technical or scientific terms. There are widespread challenges related to identifying terms belonging to different dialects, regions, and, of course, different native languages. In today’s ever-connected world, we must consider the instances where cross-cultural communication across any form of media will be prevalent.
Scribie’s Human-in-the-Loop Approach: A Solution to Language Diversity
Recognizing these limitations, we have pioneered a hybrid approach that combines ASR technology with human expertise. Their human-in-the-loop model ensures that transcriptions undergo multiple rounds of human review, effectively addressing the challenges of diverse accents and languages. This approach has proven particularly valuable for non-English content, content related to technical aspects, and heavily accented speech, where traditional ASR systems might often falter.
Technical Content: From Challenge to Opportunity
Regarding technical content, conventional ASR systems face significant challenges with high error rates for specialized terminology. However, Scribie’s processes ensure that the complex technical terminology across various fields is not lost in transcription. They are accurately transcribed, and industry-specific terms are verified, ensuring precision in fields ranging from medicine and law to engineering and scientific research.
There could at times be content that is mis-captured by ASR even though it would be part of regular language and such instances contribute to a loss in accuracy. There have been some examples too where the word ‘crews’ was recognised as ‘proves’, ‘chow’ was recognised as ‘child’, ‘pilots, bombardiers, navigators’ was not recognised at all. There is no way to overcome these smaller mistakes unless there is human intervention.
Speaking of native language terms used while conversing in English pose their own set of challenges and quality control processes are much more effective there.
Speaker Diarization: Human Intelligence Meets Artificial Intelligence
While traditional ASR struggles with speaker diarization, particularly in scenarios involving multiple speakers or overlapping dialogue, Scribie’s hybrid approach excels. Their human reviewers can easily distinguish between speakers, identify overlapping conversations, and accurately attribute statements to the correct speakers. This combination of technology and human expertise achieves near-perfect speaker identification, even in challenging scenarios with multiple participants or varying acoustic conditions.
Achieving 99.9% Accuracy: The Power of Human-in-the-Loop
Scribie’s commitment to accuracy is demonstrated through its rigorous quality control process. Each transcription goes through our process of Human-in-the-Loop, with checks for accuracy. This systematic approach enables us to achieve and maintain a remarkable 99.9% accuracy rate, being a savior for our clients. This far surpasses the capabilities of standalone ASR systems. Our team uses comprehensive and robust in-house tools that significantly help them work at scale and speed.
The Future of Transcription Services
As the industry evolves, Scribie’s human-in-the-loop model represents the future of transcription services. This approach demonstrates that the solution to ASR’s current limitations lies not in completely replacing human expertise with artificial intelligence but in finding the optimal balance between the two.
The company continues to refine its processes, using insights from human reviewers to improve its ASR systems while maintaining the crucial element of human oversight. This iterative improvement process ensures that their service continues to evolve and improve, adapting to new challenges and user needs.