- MWS AI, a prominent developer of speech recognition technologies, has announced the release of two new open-source tools designed to enhance and analyze the quality of Automatic Speech Recognition (ASR) models.
- The first introduced tool is RESON (Research Engine for Speech & Observation Notes).
- The second significant announcement is RESON OSD, an open benchmark specifically tailored for evaluating Russian-language speech recognition models.
MWS AI Launches RESON for Advanced ASR Evaluation
MWS AI, a prominent developer of speech recognition technologies, has announced the release of two new open-source tools designed to enhance and analyze the quality of Automatic Speech Recognition (ASR) models. These innovations, developed internally by the ASR ML team under the Audiogram ASR/TTS platform, are now publicly accessible.
RESON: A Comprehensive Tool for ASR Quality Analysis
The first introduced tool is RESON (Research Engine for Speech & Observation Notes). It is engineered for the comprehensive analysis of ASR model quality. RESON provides a platform that enables researchers and developers to gain a more detailed understanding of their speech recognition systems’ performance, moving beyond traditional metrics.
RESON OSD: An Open Benchmark for Russian-Language ASR Systems
The second significant announcement is RESON OSD, an open benchmark specifically tailored for evaluating Russian-language speech recognition models. This dataset aims to standardize and streamline the process of comparing and improving ASR systems that handle the Russian language, particularly within telephony channels. Its introduction offers a valuable resource for the scientific community and industry striving to enhance the accuracy and reliability of Russian-language solutions.
According to Alexander Shevchenko, Tech Lead of the ASR ML team at MWS AI, these tools are the culmination of a year’s internal development and are intended to further advance speech recognition technologies.
RESON sounds like a game-changer, especially for us working with less common languages in ASR. I’ve been struggling to get consistent benchmarks for Russian telephony data, and the existing tools often fall short on nuances specific to call center audio. My current setup involves a lot of manual transcription checks and custom scripts to catch those edge cases like overlapping speech or low-quality recordings. If RESON OSD can standardize even a fraction of that, it’ll save me countless hours. I’m keen to see if it handles accents and varied speaking speeds well, as that’s where most models falter. My tip: always test with real-world, noisy data; synthetic data only gets you so far.