GOP and LPR score

[wangkenpu]

Recently, I find 2 parts are different from the original Hu's GOP paper.

1. LPR computation

   // LPR(p_j|p_i)=\log p(p_j|\mathbf o; t_s, t_e)-\log p(p_i|\mathbf o; t_s, t_e)
   for (int k = 0; k < phone_num; k++)
     phone_level_feat(1 + phone_num + k) = lpp_part(phone_id) - lpp_part(k);

   Per my understanding, $p_i$ should be the canonical phoneme, $LPR(p_j|p_i) = \log p(p_j|\mathbf o; t_s, t_e) - \log p(p_i|\mathbf o; t_s, t_e)$, and phoneme level feature is defined as ${[LPP(p_1),\cdots,LPP(p_M), LPR(p_1|p_i), \cdots, LPR(p_j|p_i),\cdots]}^T$. So I think the above code should be changed as:

   // LPR(p_j|p_i)=\log p(p_j|\mathbf o; t_s, t_e)-\log p(p_i|\mathbf o; t_s, t_e)
   for (int k = 0; k < phone_num; k++)
     phone_level_feat(1 + phone_num + k) = pp_part(k) - lpp_part(phone_id);

2. Formulation to compute the GOP score in the document

   In the document,

   $$GOP(p)=\log \frac{LPP(p)}{\max_{q\in Q} LPP(q)}$$

   $$LPP(p)=\log p(p|\mathbf o; t_s,t_e)$$

   In Hu's paper

   $$GOP(p)=\log \frac{p(p|\mathbf o; t_s,te)}{\max{q\in Q} p(q|\mathbf o; t_s,t_e)}$$

   Thus, I think the GOP formulation in the document should be changed to

   $$GOP(p)=\frac{LPP(p)}{\max_{q\in Q} LPP(q)}$$

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[wangkenpu]

ping

[jimbozhang]

Sorry for the delayed reply. @wangkenpu

Regarding the LPR computation, I agree with you but I believe it should not significantly impact the result.

Regarding the document, I appreciate you pointed out the redundancy \log in the equation. It should be nice if you could submit a pull request to address this issue.