Effect of frequency weighting on MLP-based speaker canonicalization

Yuichi Kubota*, Motoi Omachi, Tetsuji Ogawa, Tetsunori Kobayashi, Tsuneo Nitta


研究成果: Conference article査読


Accurate and efficient speaker canonicalization is proposed to improve the performance of speaker-independent ASR systems. Vocal tract length normalization (VTLN) is often applied to speaker canonicalization in ASR; however, it requires parallel decoding of speech when estimating the optimal warping parameter. In addition, VTLN provides the same linear spectral transformation in an utterance, although optimal mapping functions differ among phonemes. In this study, we propose a novel speaker canonicalization using multilayer perceptron (MLP) that is trained with a data set of vowels to map an input spectrum to the output spectrum of a standard speaker or a canonical speaker. The proposed speaker canonicalization operates according to the integration of MLP-based mapping and identity mapping that depends on frequency bands and achieves accurate recognition without any tuning of mapping function during run-time. Results of experiments conducted with a continuous digit recognition task showed that the proposed method reduces the intra-class variability in both of the vowel and consonant parts and outperforms VTLN.

ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版ステータスPublished - 2014 1月 1
イベント15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, Singapore
継続期間: 2014 9月 142014 9月 18

ASJC Scopus subject areas

  • 言語および言語学
  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • モデリングとシミュレーション


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