Summary of Performance Evaluation Of Slam-asr: the Good, the Bad, the Ugly, and the Way Forward, by Shashi Kumar et al.
Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward
by Shashi Kumar, Iuliia Thorbecke, Sergio Burdisso, Esaú Villatoro-Tello, Manjunath K E, Kadri Hacioğlu, Pradeep Rangappa, Petr Motlicek, Aravind Ganapathiraju, Andreas Stolcke
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the limitations of using linear connectors between speech foundation encoders and large language models (LLMs) in achieving strong Automatic Speech Recognition (ASR) capabilities. Despite previous research demonstrating impressive results, it is unclear whether these approaches are robust across different scenarios and speech conditions, such as domain shifts and speech perturbations. The study conducts ablation experiments using the SLAM-ASR approach to identify effective strategies for utilizing LLM-based ASR models in various settings. Key findings include poor performance in cross-domain evaluation settings and significant degradation of performance due to speech perturbations on in-domain data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well a special kind of AI model works when it’s used to recognize spoken words. The researchers tried different ways of using this model, called SLAM-ASR, to see if it could work well in different situations and with different types of speech. They found that the model doesn’t do very well when it’s tested on speech from a different source or when there are some changes to the speech, like talking faster or adding noise. |
