In the AI/ML ecosystem, where numerous Automatic Speech Recognition (ASR) service providers strive to deliver high-quality models, the benefit of benchmarking for translation against industry alternatives serves as an invaluable tool.

The speech and voice recognition market is growing rapidly, reaching billions, the drive to become an industry leader is high amongst ASR service providers. Allowing for an enhanced and thorough understanding of model improvements, an objective benchmarking service supports ASR service providers in becoming the standard of model quality and service.

As more languages, dialects, and domains are demanded by the AI/ML market, it is becoming increasingly difficult to track model performance. 

With aiXplain’s benchmarking for transcription service, members can now easily compare different ASR models while leveraging insightful industry-proven scoring metrics providing an in-depth analysis of model performance. 

Here are the 5 reasons for model suppliers to use aiXplain’s benchmarking for transcription tools: 

Detailed, Objective Reporting System

Our benchmarking for translation reporting system considers transcription accuracy, speed, and availability on generic and niche domains, covering several languages on publicly available or proprietary speech datasets. To gain valuable insights for model improvement, ASR service providers can rely on these detailed, objective reports that are accompanied by expert-level recommendations. 

The reports capture potential biases and deficiencies in models enabling ASR service providers to effectively debug such issues all while comparing their model status with industry alternatives. 

Industry-Proven Metrics

Covering more ASR-relevant metrics than any other standard benchmarking for translation tool, aiXplain provides their model performance analyses using industry-proven metrics. Such metrics include word error rate (WER), character error rate (CER), Word Info Reserved (WIP), Word Info Lost (WIL), Match Error Rate (MER), and more to come!

Each of these metrics provides a comprehensive performance analysis that allows for model interpretation covering various factors. 

Periodical Scheduling

With the option to schedule reports at desired frequencies, ASR service providers can remain updated with their service performance status against industry alternatives. 

This also allows ASR service providers to gain valuable regression insights into model development and performance metrics on a periodical basis. Essentially, allowing for the ability to debug potential performance-related issues as they arise. 

Onboard Proprietary Datasets

aiXplain’s benchmarking for translation jobs also allow for the ability to onboard proprietary datasets fulfilling any potential custom requirements. This allows ASR service providers to achieve meaningful benchmarking results that are supported by datasets that are relevant to their needs. 

Essentially, ASR service providers can take full advantage of this benchmarking service with datasets that are most relevant to their individual needs. 

Efficient And Cost-Effective Solution

The high resource allocation demanded by the development and implementation of robust benchmarking, and reporting infrastructures can complicate the process of building in-house benchmarking systems posing a challenge for many ASR service providers. Hence, solving this in-house is simply an added burden and cost to most enterprises and rarely serves as a cost-effective expenditure.

aiXplain’s benchmarking service resolves this challenge resulting in up to 90% reduction in benchmark-related workload. Even large enterprises equipped with benchmarking teams can optimize their budgets by up to 40%. 

Organizations looking for benchmarking for transcription services for their ASR models can now receive a detailed, objective performance assessment on aiXplain. 

Join our private beta to get started!

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