Mel-frequency Domain Research Articles

An Improved Multi-band Spectral Subtraction using Mel-scale

In order to improve the intelligibility and quality of speech signal under different noise environments, an improved multi-band spectral subtraction algorithm using Mel-scale is proposed in this paper. In this algorithm, the whole speech spectrum is divided into N subbands without overlapping each other using the Mel frequency domain, and uses spectral subtraction to obtain the estimated pure speech independently in each subband. By using various types of noise at various SNRs, the experimental data, including SNR, PESQ measure and non-professional hearing test, are obtained. The result show that the algorithm is superior to the multi-band spectral subtraction with uniform segmentation and the traditional spectral subtraction algorithm, without causing considerable signal distortion and remnant musical noise.

Foreground Speech Segmentation and Enhancement Using Glottal Closure Instants and Mel Cepstral Coefficients

In this paper, the speech signal recorded from the desired speaker close to microphone in natural environment is regarded as foreground speech and rest of the interfering sources as background noise . The proposed paper exploits speech production features like glottal closure instants in time domain and vocal tract information in spectral domain to segment the desired speaker's speech and to further enhance it. The foreground speech is perceptually enhanced using the auditory perception feature in mel-frequency domain using mel-cepstral coefficients and its inversion using mel log spectrum approximation filter. The focus is on enhancing the production and perceptual features of foreground speech rather than relying on modeling the interfering sources. The speech data are collected in different natural environments from different speakers in order to evaluate the proposed method. The enhanced speech signals derived at three different stages of the proposed method are evaluated with state-of-the-art methods in terms of subjective and objective measures. The proposed method provides improved performance compared to the considered state-of-the-art methods. In terms of the proposed objective measure foreground to background Ratio , the enhancement approach presented in this paper gives an average improvement of 12 dB as opposed to existing spectral subtraction-based method which provides 3 dB. Moreover, subjective evaluation using 24 different subjects corroborates the objective test results.

Speech enhancement using hidden Markov models in Mel-frequency domain

Hidden Markov model (HMM)-based minimum mean square error speech enhancement method in Mel-frequency domain is focused on and a parallel cepstral and spectral (PCS) modeling is proposed. Both Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented for speech enhancement. To estimate clean speech waveform from a noisy signal, an inversion from the Mel-frequency domain to the spectral domain is required which introduces distortion artifacts in the spectrum estimation and the filtering. To reduce the corrupting effects of the inversion, the PCS modeling is proposed. This method performs concurrent modeling in both cepstral and magnitude spectral domains. In addition to the spectrum estimator, magnitude spectrum, log-magnitude spectrum and power spectrum estimators are also studied and evaluated in the HMM-based speech enhancement framework.The performances of the proposed methods are evaluated in the presence of five noise types with different SNR levels and the results are compared with several established speech enhancement methods especially auto-regressive HMM-based speech enhancement. The experimental results for both subjective and objective tests confirm the superiority of the proposed methods in the Mel-frequency domain over the reference methods, particularly for non-stationary noises.

VTLN Using Analytically Determined Linear-Transformation on Conventional MFCC

In this paper, we propose a method to analytically obtain a linear-transformation on the conventional Mel frequency cepstral coefficients (MFCC) features that corresponds to conventional vocal tract length normalization (VTLN)-warped MFCC features, thereby simplifying the VTLN processing. There have been many attempts to obtain such a linear-transformation, but all the previously proposed approaches either modify the signal processing (and therefore not conventional MFCC), or the linear-transformation does not correspond to conventional VTLN-warping, or the matrices being estimated and are data dependent. In short, the conventional VTLN part of an automatic speech recognition (ASR) system cannot be simply replaced with any of the previously proposed methods. Umesh proposed the idea to use band-limited interpolation for performing VTLN-warping on MFCC using plain cepstra. Motivated from this work, Panchapagesan and Alwan proposed a linear-transformation to perform VTLN-warping on conventional MFCC. However, in their approach, VTLN warping is specified in the Mel-frequency domain and is not equivalent to conventional VTLN. In this paper, we present an approach which also draws inspiration from the work of Umesh , and which we believe for the first time performs conventional VTLN as a linear-transformation on conventional MFCC using the ideas of band-limited interpolation. Deriving such a linear-transformation to perform VTLN, would allow us to use the VTLN-matrices in transform-based adaptation framework with its associated advantages and yet would require the estimation of a single parameter. Using four different tasks, we show that our proposed approach has almost identical recognition performance to conventional VTLN on both clean and noisy speech data.

Hidden-Markov-model-based voice activity detector with high speech detection rate for speech enhancement

A new voice activity detection (VAD) algorithm with soft decision output in Mel-frequency domain is developed based on hidden Markov model (HMM) and is incorporated in an HMM-based speech enhancement system. The proposed VAD uses a two-state ergodic HMM representing speech presence and speech absence. The states are constructed from noisy speech and noise HMMs used in the speech enhancement system. This composite model provides a robust detection of speech segments in the presence of noise and obviates the need for extra modeling in HMM-based speech enhancement applications. As the main purpose of the proposed VAD is to detect speech segments accurately, a hang-over mechanism is proposed and is applied on the output of the VAD to improve the speech detection rate. The VAD is integrated in the HMM-based speech enhancement system in Mel-frequency spectral (MFS) and cepstral (MFC) domains. The performance of the proposed VAD, the effectiveness of the hang-over mechanism and the performance of the VAD-integrated speech enhancement system are evaluated on four noise types at different SNR levels. The experimental results confirm the superiority of the proposed VAD compared to the reference methods particularly for speech detection rate at the dominant noisy conditions.

Channel Robust Feature Transformation Based on Filter-Bank Energy Filtering

This correspondence proposes a novel feature transform for channel robustness with short utterances. In contrast to well-known techniques based on feature trajectory filtering, the presented procedure aims to reduce the time-varying component of channel distortion by applying a bandpass filter along the Mel frequency domain on a frame-by-frame basis. By doing so, the channel cancelling effect due to conventional feature trajectory filtering methods is enhanced. The filtering parameters are defined by employing a novel version of relative importance analysis based on a discriminant function. Experiments with telephone speech on a text-dependent speaker verification task show that the proposed scheme can lead to reductions of 8.6% in equal error rate when compared with the baseline system. Also, when applied in combination with cepstral mean normalization and RASTA, the presented technique leads to further reductions of 9.7% and 4.3% in equal error rate, respectively, when compared with those methods isolated.

On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.