In this paper, we propose a joint optimal method for automatic speech recognition (ASR) and ideal binary mask (IBM) estimation in transformed into the cepstral domain through a newly derived generalized expectation maximization algorithm. First, cepstral domain missing feature marginalization is established using a linear transformation, after tying the mean and variance of non-existing cepstra...

Developmental changes in the human speech production system signal age-dependent variability in the speech signal properties. In this paper, an informationtheoretic analysis of developmental changes in the speech signal is presented. The effects of age and signal bandwidth on speech signal features are analyzed especially motivated by implications to automatic recognition of children's speech. ...

It is common practice to use similar or even the same feature extraction methods for automatic speech recognition and speaker identification. While the front-end for the former requires to preserve phoneme discrimination and to compensate for speaker differences to some extend, the front-end for the latter has to preserve the unique characteristics of individual speakers. It seems, therefore, c...

Cepstral coefficients derived either through linear prediction (LP) analysis or from filter bank are perhaps the most commonly used features in currently available speech recognition systems. In this paper, we propose spectral subband centroids as new features and use them as supplement to cepstral features for speech recognition. We show that these features have properties similar to formant f...

We work on a special case of the speech inversion problem, namely the mapping from Mel Frequency Cepstral Coeeficients onto articulatory trajectories, derived by EMA. We employ Support Vector Regression, and use PCA and ICA as means to account for the spatial structure of the problem. Our results are comparable to those achieved by older attempts on the same task, indicating probably some natur...

In this paper, a new approach for linear prediction (LP) analysis is explored, where predictor can be computed from a mel-warped subband-based autocorrelation functions obtained from the power spectrum. For spectral representation a set of multi-resolution cepstral features are proposed. The general idea is to divide up the full frequency-band into several subbands, perform the IDFT on the mel ...

In this paper we introduce a robust feature extractor, dubbed as Modified Function Cepstral Coefficients (MODFCC), based on gammachirp filterbank, Relative Spectral (RASTA) and Autoregressive Moving-Average (ARMA) filter. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. In speech recognition systems Mel-Fr...

Several features were compared with regard to recognition performance in a musical instrument recognition system. Both mel-frequency and linear prediction cepstral and delta cepstral coefficients were calculated. Linear prediction analysis was carried out both on a uniform and a warped frequency scale, and reflection coefficients were also used as features. The performance of earlier described ...

In this paper, we propose an ADPCM coder which uses a backward adaptive predictor based on the adap tive mel-cepstral analysis. The spectrum represented by the mel-cepstral coefficients has frequency resolution similar to that of the human ear which has high resolution at low frequencies. In the coder, since the transfer functions of noise shaping and postflltering are also defined through the ...

Conventional methods for incorporating temporal information into speech features apply regression to a series of successive cepstral vectors to generate differential cepstra, or apply a cosine transform to generate cepstral-time matrices. This paper aims to generalise these techniques such that a series of stacked cepstral vectors is multiplied by a temporal transform matrix to produce the fina...