A fast algorithm for parallel model combination for noisy speech recognition

✍ Scribed by Tai-Hwei Hwang; Hsiao-Chuan Wang

• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 223 KB
• Volume: 14
• Category: Article
• ISSN: 0885-2308
• DOI: 10.1006/csla.2000.0137

✦ Synopsis

Based on the log-normal assumption, parallel model combination (PMC) provides an effective method to adapt the cepstral means and variances of speech models for noisy speech recognition. In addition, the log-add method has been derived to adapt the mean by ignoring the cepstral variance during the process of PMC. This method is efficient for speech recognition in a high signal-to-noise ratio (SNR) environment. In this paper, a new interpretation of the log-add method is proposed. This leads to a modified scheme for performing the adaptation procedure in PMC. This modified method is shown to be efficient in improving recognition accuracy in low SNR. Based on this modified PMC method, we derive a direct adaptation procedure for the variance of speech models in the cepstral domain. The proposed method is a fast algorithm because the computation for the transformation of the covariance matrix is no longer required. Three recognition tasks are conducted to evaluate the proposed method. Experimental results show that the proposed technique not only requires lower computational cost but it also outperforms the original PMC technique in noisy environments.