  While existing literature relies on performance differences to uncover gender
biases in ASR models, a deeper analysis is essential to understand how gender
is encoded and utilized during transcript generation. This work investigates
the encoding and utilization of gender in the latent representations of two
transformer-based ASR models, Wav2Vec2 and HuBERT. Using linear erasure, we
demonstrate the feasibility of removing gender information from each layer of
an ASR model and show that such an intervention has minimal impacts on the ASR
performance. Additionally, our analysis reveals a concentration of gender
information within the first and last frames in the final layers, explaining
the ease of erasing gender in these layers. Our findings suggest the prospect
of creating gender-neutral embeddings that can be integrated into ASR
frameworks without compromising their efficacy.
