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@ -11,15 +11,15 @@ We propose BTC/OTC to directly train an ASR system leveraging weak supervision,
<div style="display: flex;flex; justify-content: space-between">
<figure style="flex: 2; text-align: center; margin: 5px;">
<img src="figures/sub.png" alt="Image 1" width="30%" />
<figcaption>Substitution error</figcaption>
</figure>
<figure style="flex: 2; text-align: center; margin: 5px;">
<img src="figures/ins.png" alt="Image 2" width="30%" />
<figcaption>Insertion error</figcaption>
</figure>
<figure style="flex: 2; text-align: center;margin: 5px;">
<img src="figures/del.png" alt="Image 3" width="30%" />
<figcaption>Deletion error</figcaption>
</figure>
</div>
<figcaption> Examples of error in the transcript. The grey box is the verbatim transcript and the red box is the inaccurate transcript. Inaccurate words are marked in bold.</figcaption> <br>
@ -27,11 +27,10 @@ We propose BTC/OTC to directly train an ASR system leveraging weak supervision,
This is achieved by using a special token $\star$ to model uncertainties (i.e., substitution errors, insertion errors, and deletion errors)
within the WFST framework during training.\
we modify $G(\mathbf{y})$ by adding self-loop arcs into each state and bypass arcs into each arc.
<div style="text-align: center;">
<figure text-align: center>
<p align="center">
<img src="figures/otc_g.png" alt="Image Alt Text" width="50%" />
<figcaption>OTC WFST representations of the transcript "a b"</figcaption>
</figure>
</p>
</div>
After composing the modified WFST $G_{\text{otc}}(\mathbf{y})$ with $L$ and $T$, the OTC training graph is shown in this figure:
@ -43,7 +42,7 @@ After composing the modified WFST $G_{\text{otc}}(\mathbf{y})$ with $L$ and $T$,
The $\star$ is represented as the average probability of all non-blank tokens.
<p align="center">
<img src="figures/otc_emission.drawio.png" alt="Image Alt Text" width="50%" />
<img src="figures/otc_emission.drawio.png" alt="Image Alt Text" width="50%">
OTC emission WFST
</p>