Cepstrum

About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.

Lombard effect compensation and noise suppression for noisy Lombard speech recognition

Abstract: The performance of a speech recognition system degrades rapidly in the presence of ambient noise. To reduce the degradation, a degradation model is proposed which represents the spectral changes in a speech signal uttered in a noisy environment. The model uses frequency warping and amplitude scaling of each frequency band to simulate the variations of formant location, formant bandwidth, pitch, spectral tilt and energy in each frequency band by the Lombard effect. Another Lombard effect-the variation of overall vocal intensity-is represented by a multiplicative constant term depending on the spectral magnitude of the input speech. The noise contamination is represented by an additive term in the frequency domain. According to this degradation model, the cepstral vector of clean speech is estimated from that of noisy-Lombard speech using spectral subtraction, spectral magnitude normalization, band-pass filtering in the Lin-Log spectral domain, and multiple linear transformations. Noisy Lombard speech data is collected by simulating noisy environments using noises from automobiles, an exhibition hall, telephone booths in downtown crowded streets, and computer rooms. The proposed method significantly reduces error rates in the recognition of 50 Korean words. For example, the recognition rate is 95.91% with this method and 79.68% without this method at an SNR (signal-to-noise ratio) 10 dB.

A performance comparison of pitch extraction algorithms for noisy speech

Abstract: Results of a performance comparison study of eight pitch extraction algorithms for noisy as well as clean speech are presented. These algorithms are the autocorrelation method with center clipping, the autocorrelation method with modified center clipping, the simplified inverse filter tracking (SIFT) method, the average magnitude difference function (AMDF) method, the pitch detection method based on LPC inverse filtering and AMDF, the data reduction method, the parallel processing method and the cepstrum method. It has been found that for pitch detection of noisy speech the algorithm that uses an AMDF or an autocorrelation function yields relatively good performance than others. A pitch detector that uses center clipped speech as an input signal is effective in pitch extraction of noisy speech. In general, preprocessing such as LPC inverse filtering or center clipping of input speech yields remarkable improvement in pitch detection.

Method for early recognition and prediction of unit damage or wear in machine plant, particularly mobile plant, based on vibration analysis with suppression of interference frequencies to improve the reliability of diagnosis

Abstract: Method and device for monitoring machine plant based on vibration monitoring whereby the data is transformed into the frequency domain and back into the time domain to allow suppression of interference frequencies. According to the method structural noise of the machine plant is recorded using a sensor (1), transmitted as an acceleration signal and analyzed in a digital signal processor (DSP). To inhibit interference due to environmental vibrations or structural sound waves that do not relate to the status of the machine plant, the signal is transformed into the frequency domain using an FFT. It is then transformed back into the time domain using a cepstrum analysis such that single shock impulses (a cepstrum) are obtained in the time domain. The cepstrum is compared with a comparison value contained in memory that corresponds to load and speed signals for the current operating status. If the threshold is exceeded, conclusions can be made about damage to the unit and a remaining service life predicted. An emergency operation can be initiated.

Speaker independent continuous speech and isolated digit recognition using VQ and HMM

Abstract: The main objective of this paper is to explore the effectiveness of perceptual features for performing isolated digits and continuous speech recognition. The proposed perceptual features are captured and code book indices are extracted. Expectation maximization algorithm is used to generate HMM models for the speeches. Speech recognition system is evaluated on clean test speeches and the experimental results reveal the performance of the proposed algorithm in recognizing isolated digits and continuous speeches based on maximum log likelihood value between test features and HMM models for each speech. Performance of these features is tested on speeches randomly chosen from “TI Digits_1”, “TI Digits_2” and “TIMIT” databases. This algorithm is tested for VQ and combination of VQ and HMM speech modeling techniques. Perceptual linear predictive cepstrum yields the accuracy of 86% and 93% for speaker independent isolated digit recognition using VQ and combination of VQ & HMM speech models respectively. This feature also gives 99% and 100% accuracy for speaker independent continuous speech recognition by using VQ and the combination of VQ & HMM speech modeling techniques.