Topic
Cepstrum
About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.
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TL;DR: This new approach implements a blind equalization scheme where an adaptive filter, using some known statistics about the signals, deconvolves the channel from the transmitted signal and shows that reducing the channel effects significantly improves the recognition performance.
26 citations
TL;DR: The capacity of APT-based speaker adaptation to achieve word error rate reductions superior to those obtained with other popular adaptation techniques, and moreover, reductions that are additive with those provided by VTLN are demonstrated.
26 citations
19 Apr 1994
TL;DR: The aim of this paper is to show that OSALPC also achieves good performance in a case of real noisy speech (in a car environment), and to explore its combination with several robust similarity measuring techniques, showing that its performance improves by using cepstral liftering, dynamic features and multilabeling.
Abstract: The performance of the existing speech recognition systems degrades rapidly in the presence of background noise. The OSALPC (one-sided autocorrelation linear predictive coding) representation of the speech signal has shown to be attractive for speech recognition because of its simplicity and its high recognition performance with respect to the standard LPC in severe conditions of additive white noise. The aim of this paper is twofold: (1) to show that OSALPC also achieves good performance in a case of real noisy speech (in a car environment), and (2) to explore its combination with several robust similarity measuring techniques, showing that its performance improves by using cepstral liftering, dynamic features and multilabeling. >
26 citations
Proceedings Article•
01 Jan 1998
TL;DR: This paper describes the attempt towards speech inverse mapping by using the mel-frequency cepstrum coe cients to represent the acoustic parameters of the speech signal by using an articulatory-acoustic codebook derived from Maeda's articulatory model.
Abstract: Recovering vocal tract shapes from the speech signal is a well known inversion problem of transformation from the articulatory system to speech acoustics. Most of the studies on this problem in the past have been focused on vowels. There have not been general methods e ective for recovering the vocal tract shapes from the speech signal for all classes of speech sounds. In this paper we describe our attempt towards speech inverse mapping by using the mel-frequency cepstrum coe cients to represent the acoustic parameters of the speech signal. An inversion method is developed based on Kalman ltering and a dynamic-system model describing the articulatory motion. This method uses an articulatory-acoustic codebook derived from Maeda's articulatory model.
26 citations
TL;DR: Experiments on NIST 2003 and 2007 LRE evaluation corpora show that TFC is more effective than SDC, and that the GMM-based BDHLDA results in lower equal error rate (EER) and minimum average cost (Cavg) than either TFC or SDC approaches.
Abstract: The shifted delta cepstrum (SDC) is a widely used feature extraction for language recognition (LRE). With a high context width due to incorporation of multiple frames, SDC outperforms traditional delta and acceleration feature vectors. However, it also introduces correlation into the concatenated feature vector, which increases redundancy and may degrade the performance of backend classifiers. In this paper, we first propose a time-frequency cepstral (TFC) feature vector, which is obtained by performing a temporal discrete cosine transform (DCT) on the cepstrum matrix and selecting the transformed elements in a zigzag scan order. Beyond this, we increase discriminability through a heteroscedastic linear discriminant analysis (HLDA) on the full cepstrum matrix. By utilizing block diagonal matrix constraints, the large HLDA problem is then reduced to several smaller HLDA problems, creating a block diagonal HLDA (BDHLDA) algorithm which has much lower computational complexity. The BDHLDA method is finally extended to the GMM domain, using the simpler TFC features during re-estimation to provide significantly improved computation speed. Experiments on NIST 2003 and 2007 LRE evaluation corpora show that TFC is more effective than SDC, and that the GMM-based BDHLDA results in lower equal error rate (EER) and minimum average cost (Cavg) than either TFC or SDC approaches.
26 citations