雜訊環境下應用線性估測編碼於特徵時序列之強健性語音辨識

Employing Linear Prediction Coding in Feature Time Sequences for Robust Speech Recognition in Noisy Environments

范顥騰(Hao-Teng Fan) ； 曾文俞(Wen-Yu Tseng) ； 洪志偉(Jeih-Weih Hung)

《中文計算語言學期刊》 18卷4期 (2013/12) Pp. 115-131

摘要

在本論文裡，我們提出了一種藉由線性估測編碼來強化語音辨識中特徵之抗噪性的新方法，在此方法中，根據線性估測編碼技術，將語音倒頻譜特徵時間序列分解出估測誤差成分後，將此估測誤差成分從原特徵序列扣除，所得的新特徵序列，相對於原始特徵序列而言，發現具有更佳的雜訊強健性，在Aurora-2此包含各類雜訊之數字語料庫的實驗環境下，經過各種預強健化處理之倒頻譜語音特徵，再進一步藉由我們所提之新方法處理後，都能得到更佳的辨識效能，且在線性估測階數很低的情況下，就可有效提升辨識率，顯示了我們可以高效率地執行實現所提之新技術。

關鍵字

線性估測編碼 ； 特徵時間序列 ； 雜訊強健性

並列摘要

In this paper, we present a novel method to extract noise-robust speech feature representation in speech recognition. This method employs the algorithm of linear predictive coding (LPC) on the feature time series of mel-frequency cepstral coefficients (MFCC). The resulting linear predictive version of the feature time series, in which the linear prediction error component is removed, reveals more noise-robust than the original one, probably because the prediction error portion corresponding to the noise effect is alleviated accordingly. Experiments conducted on the Aurora-2 connected digit database shows that the presented approach can enhance the noise robustness of various types of features in terms of significant improvement in recognition performance under a wide range of noise environments. Furthermore, a low order of linear prediction for the presented method suffices to give promising performance, which implies this method can be implemented in a quite efficient manner.

並列關鍵字

Noise Robustness ； Speech Recognition ； Linear Predictive Coding ； Temporal Filtering

參考文獻

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