In this paper, it is shown how an automatic recognition algorithm, based on hidden Markov models (HMM), can benefit by properly utilizing findings from perceptual experiments on nasals. Perceptual studies on nasal consonants have shown that both nasal murmurs and formant transitions are important in the identification of place of articulation. Thus both acoustic segments bordering the nasal release were incorporated into this HMM-based system. A 7% improvement in alveolar recognition was obtained by explicitly modeling the vowel-nasal transition segments. Further overall improvement (6%) was realized by making the HMM recognizer ``focus'' more on the vowel-nasal transition segments bordering the nasal release, and less on the nasal murmur and vowel portion of the / m/ and / n/ syllables. An overall average [m]–[n] recognition of 95% was obtained when testing this technique on 60 speakers outside the training set.
展开▼
机译：在本文中，显示了基于隐马尔可夫模型（HMM）的自动识别算法如何通过正确利用鼻部感知实验的发现而受益。对鼻辅音的知觉研究表明，鼻腔杂音和共振峰过渡在确定发音部位时都非常重要。因此，将与鼻部释放相邻的两个声学节段合并到该基于HMM的系统中。通过对元音-鼻过渡段进行显式建模，可以使肺泡识别提高7％。通过使HMM识别器``更多地''专注于与鼻腔释放相邻的元音-鼻过渡段，而减少了/ m /和/ n /的鼻杂音和元音部分，进一步实现了总体改善（6％）。音节。在训练集之外的60位说话者身上测试这项技术时，总体平均[m] – [n]识别率为95％。
展开▼
