ASR Benchmarking, and Regression Reporting Services made simple
Today aiXplain announces Benchmarking for Transcription. This release brings comprehensive performance evaluation and reporting tools for automatic speech recognition (ASR) models to all of its members. With this service, ASR model analysis, utilizing objective industry standard scoring metrics, is now readily available to AI consumers, suppliers, program managers and machine learning specialists alike.
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In the highly competitive transcription market, it has become exceedingly difficult to maintain state-of-the-art ASR model performance across a wide variety of language, dialect, and transcription domains. To further complicate this, project managers are often required to divert engineering resources toward the development and maintenance of in-house benchmarking capabilities necessary to identify critical areas of iterative model improvement.
With aiXplain’s Benchmarking for Transcription services members can now easily schedule periodical benchmark reporting, at desired intervals, to evaluate and compare ASR models leveraging multiple industry-proven objective analysis scoring metrics. By enabling access to benchmark and regression testing aiXplain is helping members gain critical insights into areas of model improvement, which serves as an invaluable asset for evaluating model quality and performance issues.
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Premium AI/ML members can expect to reduce their benchmarking related workload by up to 90%, leveraging aiXplain’s expert analysis and model recommendation reporting. These reports appraise: transcription accuracy, speed and availability on generic and niche domains, covering several languages on publicly available or proprietary speech datasets. Even large enterprises equipped with dedicated benchmarking teams, can now enjoy impressive performance gains utilizing aiXplain’s benchmarking for transcription with as much as a 40% reduction in overhead cost.
To ensure members are able to take full advantage of these ASR benchmarking services, aiXplain facilitates custom dataset onboarding, fulfilling absolute proprietary requirements required for achieving meaningful results. Kamer Yuksel, Principal AI Architect at aiXplain, adds that “our benchmarking for transcription service provides sophisticated tools for ASR service providers to discover potential biases and deficiencies in their models, and will allow them to debug identified issues with sentence-granularity, by focusing and prioritizing their data collection strategy accordingly.”
