An efficient and scalable 2D DCT-based feature coding scheme for remote speech recognition

A 2D DCT-based approach to compressing acoustic features for remote speech recognition applications is presented. The coding scheme involves computing a 2D DCT on blocks of feature vectors followed by uniform scalar quantization, runlength and Huffman coding. Digit recognition experiments were conducted in which training was done with unquantized cepstral features from clean speech and testing used the same features after coding and decoding with 2D DCT and entropy coding and in various levels of acoustic noise. The coding scheme results in recognition performance comparable to that obtained with unquantized features at low bitrates. 2D DCT coding of MFCCs (mel-frequency cepstral coefficients) together with a method for variable frame rate analysis (Zhu and Alwan, 2000) and peak isolation (Strope and Alwan, 1997) maintains the noise robustness of these algorithms at low SNRs even at 624 bps. The low-complexity scheme is scalable resulting in graceful degradation in performance with decreasing bit rate