Chuan-Wei Ting

Learn More
We present a novel subspace modeling and selection approach for noisy speech recognition. In subspace modeling, we develop a factor analysis (FA) representation of noisy speech, which is a generalization of a signal subspace (SS) representation. Using FA, noisy speech is represented by the extracted common factors, factor loading matrix, and specific(More)
Gaussian mixture model (GMM) techniques are popular for speaker identification. Theoretically, each Gaussian function should have a full covariance matrix. However, the diagonal covariance matrix is usually used because the inverse of diagonal covariance matrix can be easily calculated via expectation maximization (EM) algorithm. This paper proposes a new(More)
This paper presents a novel streamed hidden Markov model (HMM) framework for speech recognition. The factor analysis (FA) principle is adopted to explore the common factors from acoustic features. The streaming regularities in building HMMs are governed by the correlation between cepstral features, which is inherent in common factors. Those features(More)
{cwting, bswu, chien}@chien.csie.ncku.edu.tw 摘要 在傳統語音辨識系統中,模型的訓練環境與測試環境不匹配 (mismatch)是造成辨識率下降的首要 問題,在此議題上,過去文獻已提出許多解決方法,如在語音模型端引入模型參數的不確定性所 建立的強健性貝氏預測分類 (Bayesian predictive classification)法則,或是調整模型於測試環境的 調適方法,如最大事後機率(MAP)調適以及線性迴歸(MLLR)調適,甚至進一步考慮語音模型鑑 別性之最小分類錯誤線性迴歸(MCELR)調適等方法。其中,貝氏預測分類法則是將模型參數的 不確定性(uncertainty)適當的引入決策法則以達到決策方法的強健性,而參數不確定性反應了雜(More)