加成性雜訊環境下運用特徵參數統計補償法於強健性語音辨識

Feature Statistics Compensation for Robust Speech Recognition in Additive Noise Environments

在自動語音辨識的研究上，如何有效地降低背景雜訊的影響，以增加語音辨識系統的強健性，一直是一大研究重點，其中語音特徵參數正規化法是廣為人用的強健技術之一。然而，對於多變的語音訊號該如何準確的估算出其不同時段的統計值，是影響語音特徵參數正規化法效果的一個重要因素。本論文主要是針對在加成性雜訊環境下，對不同的特徵參數提出更有效率且準確的統計值補償法，以降低加成性雜訊對語音特徵參數的影響。我們提出了運用虛擬雙通道碼簿為基礎之特徵參數補償技術，其中包含三種方法：倒頻譜統計補償法、線性最小平方回歸法與二次最小平方回歸法。我們將這些方法運用在四種不同的語音特徵參數的補償上，發現都能有效降低加成性雜訊對語音特徵的影響，進而大幅提升辨識率，同時，在與傳統以段落為基礎的特徵參數正規化技術比較下，我們所提出的方法可達到更佳的強健效果。

In this paper, we propose several compensation approaches to alleviate the effect of additive noise on speech features for speech recognition. These approaches are simple yet efficient noise reduction techniques that use online constructed pseudo stereo codebooks to evaluate the statistics in both clean and noisy environments. The process yields transforms for noise-corrupted speech features to make them closer to their clean counterparts. We apply these compensation approaches on various well- known speech features, including mel-frequency cepstral coefficients (MFCC), autocorrelation mel-frequency cepstral coefficients (AMFCC), linear prediction cepstral coefficients (LPCC) and perceptual linear prediction cepstral coefficients (PLPCC). Experimental results conducted on the Aurora-2 database show that the proposed approaches provide all types of the features with a significant performance gain when compared to the baseline results and those obtained by using the conventional utterance-based cepstral mean and variance normalization (CMVN).