Abstract
Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA), independent component analysis (ICA), and linear discriminant analysis (LDA). Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS) based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.
Highlights
In the case of distant speech recognition, system performance decreases sharply due to the effects of reverberation
Vowel /o/ has the largest (10) dimension and consonant / p/ the smallest (2) dimension. This trend means that phoneme subspaces have correlated information between each other
We proposed the new speech feature extraction method which emphasizes the phonemic information from observed speech using Principal Component Analysis (PCA), the Minimum Description Length (MDL) principle, and Independent Component Analysis (ICA)
Summary
In the case of distant (hands-free) speech recognition, system performance decreases sharply due to the effects of reverberation. To solve this problem, there have been many studies carried out on feature extraction, model adaptation, and decoding. Our proposed method focuses on the feature extraction domain. The Mel-Frequency Cepstrum Coefficient (MFCC) is a widely used speech feature. Since the feature space of a MFCC obtained using Discrete Cosine Transform (DCT) is not directly dependent on speech data, the observed signal with noise does not show good performance without utilizing noise suppression methods. There are other methods for feature extraction: RASTA-PLP [1, 2], normalization [3, 4], Principal Component Analysis (PCA) [5,6,7], Independent Component Analysis (ICA) [8, 9], and Linear Discriminant Analysis (LDA) [10] based methods
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