Assessing the Performance and Efficiency of Mamba ASR in Low-Resource Scenarios

Mamba, a state space model-based architecture, is emerging as a strong alternative to Transformer models, showing equal or superior performance in sequence generation, including speech. However, analyses have focused mainly on highresource scenarios. This paper explores Mamba’s potential in ASR for low-resource scenarios. We compare the Transformerbased Conformer and its state-space counterpart, ConMamba, across nine languages with varying training data. Our results show that ConMamba achieves similar WER to Conformer for short-context inputs but significantly improves performance on long-context inputs, reducing WER by up to 50% on average. Additionally, ConMamba enhances efficiency, requiring 40–45% less training time, using 50% less memory, and accelerating inference by 63–70%, making it a more effective ASR solution across different data availability scenarios.