## 5 Experimental Results  
By also leveraging the results obtained from our prior investigation in [17], this section reports the performance of our newly proposed disentanglement approach in comparison with the state-of-the-art methods described in Section 4.3. The results were computed on the test-clean partition of LibriSpeech. \"Original\" in both Tables 2 and 3 refers to the original audio signals without conversion.  
Firstly, considering our framework presented in [17], Table 2 presents the performance of the different pre-trained models (HuBert and WavLM) and the following two types of representations: one based on the last hidden layer and another using the posteriorgram produced by the CTC head. As expected, the results confirmed that these representations partially mask speaker's characteristics but still maintain some speaker information. A trade-off exists between the quality of the reconstructed waveform, which is represented by the WER, and the degree of anonymization obtained. Using the output of the CTC head as speech representation leads to very good anonymization, albeit at the cost of a degradation in terms of the speech quality as it only includes information about the speech content. Conversely, using a latent representation inevitably leaks information about the speaker, particularly when larger vectors are used. As per the reminder encouraged from our experimental analysis, we considered WavLM HS-768 as speech embeddings.  
Table 3 reports the performance of our proposed disentanglement approach when we considered the following two different implementations: one that uses WavLM for the speech content representation, while the other is the original S3 PRL. The latter implementation was used with and without disentanglement. Firstly, by focusing on the signal-processing methods, we can observe that applying the transformations used in [36] provides very interesting results at a low computational cost. The performance was found to be significantly better than the McAdams, i.e., the other signal-processing method. Traditional voice conversion methods (i.e., AGAIN-VC or PPG-VC) offer very good de-anonymization performance but at the cost of a high degradation of the speech signal. This degraded quality in the generated waveform may be due to a distributional mismatch between the acoustic features predicted by the traditional conversion models and those that the vocoder expects based on its training [24].  
Using the S3 PRL framework with WavLM HS-768 notably improves the performance with an EER of 37.9% while introducing only a marginal degradation in WER. Finally, the last row in Table 3 shows the considerable benefit provided by the present work's disentanglement approach, as it provided the highest EER (almost 50%) compared to all the other methods. Notably, it significantly increased performance compared to the method without disentanglement (from 37.9% to 49.4% EER) while maintaining a comparable WER (from 6.24 to 6.56). This finding suggests that the disentanglement mechanism is a viable approach to strike a balance between reducing the amount of speaker information present in a signal while also maintaining reasonable intelligibility.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **EER \(\uparrow\)** & **WER \(\downarrow\)** \\ \hline Original & 1.2 & 5.29 \\ \hline Hubert CTC-32 & 44.3 & 9.47 \\ WavLM CTC-31 & 44.6 & 9.39 \\ Hubert HS-1024 & 35.2 & 7.38 \\ WavLM HS-768 & 33.5 & 7.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Anonymization performance using [PERSON] and WavLM as pre-trained models and by considering either the exit of the last hidden layer (HS) or the posteriorgram provided by the CTC head as speech embeddings. The number near each model indicates the size of the speech embeddings. “Original” refers to the original audio signals without conversion.