第22回 ロボット聴覚特集 - 奥乃研究室 - 京都大学

1, ···,N M(k, i) 2 M(k, i) =2∏j=−2,j̸=iM(k + j, i − N )2 MFM N =48 MFM Figure 2 Figure 2 a)c)e) b)d)f) a)c)e) MFM MFM MFM 5 MFT Hidden Markov ModelHMMMFT x S j f(x|S j )L P (l|S j ) N HMM L∑b j (x) =f(x|S j )= P (l|S j )f(x|l, S j )l−1MFT b j (x) MFM M(i) {L∑N}∑b j (x) = P (l|S j )exp M(i)logf(x(i)|l, S j )l−1i−1 HMM HMM Figure 3: HMM MFT CTK Julius[11]CTK HMM Viterbi Julius Julius [14] MFT N-gram Julius 2 HMM Julius CTK 6 4 Figure 3(1) GSS (2) post-filter(3) MFM (4) MFM MFT GSS [12] 8 post-filter GSS post-filter MFM post-filter MFM 104