Mel Filter Bank Research Articles

Energy efficient and robust CSIP algorithm in distributed wireless sensor networks

This paper develops an energy efficient and robust collaborative signal and information processing (CSIP) algorithm and applies it to vehicle classification applications. The conventional algorithms collaboratively process all the time-series data from every node in the network. This signal–noise ratio (SNR)-based CSIP algorithm (SNRCSIP) collaboratively processes only the extracted features from part of the nodes. This algorithm efficiently reduces the energy consumption compared with the conventional algorithms by reducing the traffic. Apart from the energy efficiency, we demonstrate the robustness of the SNRCSIP algorithm by giving the high correct recognition ratio between the tracked vehicle and the wheeled vehicle with the acoustic features extracted by an improved form of mel filter bank (MFB), which is rarely applied in vehicle classification applications. Experimental results show that the SNRCSIP algorithm greatly reduces the energy consumption and achieves quite satisfied correct recognition ratio with the features extracted by the improved MFB.

Feature extraction from higher-lag autocorrelation coefficients for robust speech recognition

In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower-time lags, while the higher-lag autocorrelation coefficients are least affected, this method discards the lower-lag autocorrelation coefficients and uses only the higher-lag autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-lag autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further (like the well-known Mel frequency cepstral coefficient (MFCC) procedure) by the Mel filter bank, log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the autocorrelation Mel frequency cepstral coefficients (AMFCCs). We evaluate the speech recognition performance of the AMFCC features on the Aurora and the resource management databases and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech. Finally, we show that the AMFCC features perform better than the features derived from the robust linear prediction-based methods for noisy speech.

A Computationally Efficient Mel-Filter Bank VAD Algorithm for Distributed Speech Recognition Systems

This paper presents a novel computationally efficient voice activity detection (VAD) algorithm and emphasizes the importance of such algorithms in distributed speech recognition (DSR) systems. When using VAD algorithms in telecommunication systems, the required capacity of the speech transmission channel can be reduced if only the speech parts of the signal are transmitted. A similar objective can be adopted in DSR systems, where the nonspeech parameters are not sent over the transmission channel. A novel approach is proposed for VAD decisions based on mel-filter bank (MFB) outputs with the so-called Hangover criterion. Comparative tests are presented between the presented MFB VAD algorithm and three VAD algorithms used in the G.729, G.723.1, and DSR (advanced front-end) Standards. These tests were made on the Aurora 2 database, with different signal-to-noise (SNRs) ratios. In the speech recognition tests, the proposed MFB VAD outperformed all the three VAD algorithms used in the standards by 14.19% relative (G.723.1 VAD), by 12.84% relative (G.729 VAD), and by 4.17% relative (DSR VAD) in all SNRs.

Human and machine consonant recognition

Three traditional ASR parameterizations matched with Hidden Markov Models (HMMs) are compared to humans for speaker-dependent consonant recognition using nonsense syllables degraded by highpass filtering, lowpass filtering, or additive noise. Confusion matrices were determined by recognizing the syllables using different ASR front ends, including Mel-Filter Bank (MFB) energies, Mel-Filtered Cepstral Coefficients (MFCCs), and the Ensemble Interval Histogram (EIH). In general the MFB recognition accuracy was slightly higher than the MFCC, which was higher than the EIH. For syllables degraded by lowpass and highpass filtering, automated systems trained on the degraded condition recognized the consonants as well as humans. For syllables degraded by additive speech-shaped noise, none of the automated systems recognized consonants as well as humans. The greatest advantage displayed by humans was in determining the correct voiced/unvoiced classification of consonants in noise.

On the Use of Channel-Attentive MFCC for Robust Recognition of Partially Corrupted Speech

This letter proposes a channel-attentive mel frequency cepstral coefficient (CAMFCC) method to improve the utilization of uncorrupted or more reliable frequency bands for robust speech recognition. This method obtains a channel attention matrix by reliability estimation of mel filter bank channels, and both the input mel frequency cepstral coefficients and the mean vectors of hidden Markov models are corrected using the channel attention matrix at the output probability calculation of the Viterbi decoding. Experimental results on the TIDIGITS database corrupted by various band-selective noises indicated that the proposed CAMFCC method utilizes the uncorrupted partial frequency bands better than a multiband method, resolving the limitation of noise localization caused by the fixed boundaries of the multiband approach.

VTLN Through Frequency Warping Based on Pitch

This article describes a Vocal Tract Length Nor­ malization (VTLN) procedure through frequency warping based on pitch estimates. This procedure aims to reduce the inter-speaker variability of speech signals in order to obtain a robust automatic speech recognition system. Two additional methods are also described: one for reducing the environment variability and another for compensating the coarticulation effects on connected word pronunciation. En­ vironment variability is compensated by explicitly modeling some frequent noise phenomena. Coarticulation phenomena compensation reduces speech signal variability by modeling events that result from coarticulation between adjacent mod­ els. Inter-speaker variability removal is performed by a traditional speaker normalization method, which consists in expanding or compressing the Mel filterbank bandwidths, in order to normalize the Vocal Tract Length (VTL) of each speaker. Most of the existing methods for VTL estimation are based on formant estimation, but the difficulty of formant estimation is a known performance limitation. The proposed method over­ comes such a problem since it estimates the warping factor through pitch. The recognition results, obtained for a tele­ phone digit recognition task (with phones and sub words as units), prove that this procedure leads to similar improve­ ments to those obtained with traditional methods based on formant estimates, actually outperforming them in some sit­ uations.

Method and system for performing speech recognition

Speech recognition processing is compensated for improving robustness of speech recognition in the presence of enhanced speech signals. The compensation overcomes the adverse effects that speech signal enhancement may have on speech recognition performance, where speech signal enhancement causes acoustical mismatches between recognition models trained using unenhanced speech signals and feature data extracted from enhanced speech signals. Compensation is provided at the front end of an automatic speech recognition system by combining linear predictive coding and mel-based cepstral parameter analysis for computing cepstral features of transmitted speech signals used for speech recognition processing by selectively weighting mel-filter banks when processing frequency domain representations of the enhanced speech signals.

A comparison of signal processing front ends for automatic word recognition

This paper compares the word error rate of a speech recognizer using several signal processing front ends based on auditory properties. Front ends were compared with a control mel filter bank (MFB) based cepstral front end in clean speech and with speech degraded by noise and spectral variability, using the TI-105 isolated word database. MFB recognition error rates ranged from 0.5 to 26.9% in noise, depending on the SNR, and auditory models provided error rates as much as four percentage points lower. With speech degraded by linear filtering, MFB error rates ranged from 0.5 to 3.1%, and the reduction in error rates provided by auditory models was less than 0.5 percentage points. Some earlier studies that demonstrated considerably more improvement with auditory models used linear predictive coding (LPC) based control front ends. This paper shows that MFB cepstra significantly outperform LPC cepstra under noisy conditions. Techniques using an optimal linear combination of features for data reduction were also evaluated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>