An ALE study reveals that noise reduction techniques can negatively impact transcription accuracy in Artificial Speech Recognition (ASR) applications.
In today's digital age, the quality of communications technology can significantly enhance the way we connect and collaborate. Recent advances in Artificial Speech Recognition (ASR) technology have led to significant improvements, particularly through open-source platforms like Vosk and Whisper, which are now pivotal in sectors requiring precise and efficient transcription services.
This blog highlights the groundbreaking work in ASR done by Alcatel-Lucent Enterprise researchers Asma Trabelsi, Laurent Werey, Sébastien Warichet and Emmanuel Helbert, which was published and presented at the international scientific conference, ICAART’24. The team’s study focuses on the impact of noise reduction techniques on the transcription quality of open-source ASR engines, showcasing how innovations in this area can streamline and enhance communication.
The research compares two leading open-source ASR tools, Vosk and Whisper, using the Word Error Rate (WER) metric. The findings suggest that Whisper generally outperforms Vosk in transcription accuracy.
The team also studied the effects of applying noise reduction models like RNNoise and ASTEROID before transcription takes place. Numerical experimentations revealed that, surprisingly, noise reduction techniques can negatively impact ASR performance and cause important information to be lost.
The team’s results clearly point to the need for continuous improvement and adjustment based on the evolving demands of ASR applications. It highlights the potential for further refining noise reduction technologies and their integration into ASR systems to meet diverse user needs.
For businesses and developers, choosing the right ASR tool is crucial for maintaining data sovereignty and achieving high-quality transcription. The ALE research not only guides users in selecting suitable ASR tools but also underscores the importance of ongoing innovation in speech recognition technologies.
As we move forward, embracing advancements in ASR and noise reduction technologies will be key to unlocking more seamless, efficient and accurate communication solutions across various industries.
For more detailed insights into the study and its implications, click here.
Asma Trabelsi
Senior Data Scientist, Alcatel-Lucent EnterpriseAs a Data Scientist at ALE, Asma leads a working group aiming at integrating Artificial Intelligence (AI) into Rainbow by Alcatel-Lucent Enterprise.
Prior to joining ALE, Asma worked at Expleo Group on a number of projects focused on applying Machine Learning in industry and transportation (autonomous vehicles and trains, chatbots) for well-known French companies like Renault, PSA and the RATP.
Asma holds a Bachelor’s Degree in Business Computing from the Faculty of Sciences and Management of Nabeul, Tunisia and a Master’s and PhD in Data Science co-supervised by Institute of Management of Tunis (ISG) and Artois University in France.
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