Acoustic Model Adaptation for Speech Recognition
スポンサーリンク
概要
- 論文の詳細を見る
Statistical speech recognition using continuous-density hidden Markov models (CDHMMs) has yielded many practical applications. However, in general, mismatches between the training data and input data significantly degrade recognition accuracy. Various acoustic model adaptation techniques using a few input utterances have been employed to overcome this problem. In this article, we survey these adaptation techniques, including maximum a posteriori (MAP) estimation, maximum likelihood linear regression (MLLR), and eigenvoice. We also present a schematic view called the adaptation pyramid to illustrate how these methods relate to each other.
- 2010-09-01
著者
関連論文
- Acoustic Model Adaptation for Speech Recognition
- Acoustic Model Adaptation for Speech Recognition
- Committee-Based Active Learning for Speech Recognition
- Robust Gait-Based Person Identification against Walking Speed Variations
- Active Learning Using Phone-Error Distribution for Speech Modeling
- Spectral Subtraction Based on Non-extensive Statistics for Speech Recognition
- Active Learning Using Phone-Error Distribution for Speech Modeling