Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis

In this paper, by using the properties of the higher order statistics (HOS) of speech and noise signals, we develop an improved voice activity detection (VAD) scheme. The proposed scheme employs the logarithm of the kurtosis of the LPC residual of a speech signal and is shown to be more effective and efficient in detecting active speech in medium to low signal-to-noise ratio (SNR) conditions without being unduly affected by the variations in the signal energy. To overcome the inability of the HOS in detecting unvoiced speech, another metric (the low band to full band energy ratio) is introduced. Depending on the estimated mean SNR, the proposed scheme works adaptively in two modes: a simple mode using only the SNR, and an enhanced mode using the HOS, the low band to full band energy ratio and the SNR. This scheme is capable of avoiding unnecessary computations, while maintaining the same performance as that working only in the enhanced mode. Simulations results are presented to demonstrate the effectiveness of the proposed voice activity detection scheme.

An improved voice activity detection using higher order statistics

A communication system (e.g., a speakerphone) includes an array of microphones, a speaker, memory and a processor. The processor may perform a virtual broadside scan on the microphone array and analyze the resulting amplitude envelope to identify acoustic source angles. Each of the source angles may be further investigated with a directed beam (e.g., a hybrid superdirective/delay-and-sum beam) to obtain a corresponding beam signal. Each source may be classified as either intelligence or noise based on an analysis of the corresponding beam signal. The processor may design a virtual beam pointed at an intelligence source and having nulls directed at one or more of the noise sources. Thus, the virtual beam may be highly sensitive to the intelligence source and insensitive to the noise sources.

Forming beams with nulls directed at noise sources

This paper addresses the problem of automatic word boundary detection in the presence of noise. We first propose an adaptive time-frequency (ATF) parameter for extracting both the time and frequency features of noisy speech signals. The ATF parameter extends the TF parameter proposed by Junqua et al. (1994) from single band to multiband spectrum analysis, where the frequency bands help to make the distinction of speech and noise signals clear. The ATF parameter can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. The ATF parameter increased the recognition rate by about 3% of a TF-based robust algorithm which has been shown to outperform several commonly used algorithms for word boundary detection in the presence of noise. The ATF parameter also reduced the recognition error rate due to endpoint detection to about 20%. Based on the ATF parameter, we further propose a new word boundary detection algorithm by using a neural fuzzy network (called SONFIN) for identifying islands of word signals in a noisy environment. Due to the self-learning ability of SONFIN, the proposed algorithm avoids the need of empirically determining thresholds and ambiguous rules in normal word boundary detection algorithms. As compared to normal neural networks, the SONFIN can always find itself an economic network size in high learning speed. Our results also showed that the SONFIN's performance is not significantly affected by the size of training set. The ATF-based SONFIN achieved higher recognition rate than the TF-based robust algorithm by about 5%. It also reduced the recognition error rate due to endpoint detection to about 10%, compared to an average of approximately 30% obtained with the TF-based robust algorithm, and 50% obtained with the modified version of the Lamel et al. (1981) algorithm.

Correction to: Word Boundary Detection With Mel-Scale Frequency Bank In Noisy Environment

http://www.seas.ucla.edu/spapl/paper/panchap_ltfw.pdf

Frequency warping for VTLN and speaker adaptation by linear transformation of standard MFCC

In previous voice activity detection (VAD) approaches that use threshold, consistent accuracy cannot be achieved since the mean-value based and the histogram based threshold estimation algorithms are not robust. They strongly depend on the percentage of voice and background noise in the estimate interval. In this paper, fuzzy clustering and Bayesian information criterion are proposed to estimate the thresholds for VAD. Compared to previous algorithms, the new algorithm is more robust and heuristic-rules-free. It is insensitive to the estimated interval, and can maintain fast tracking speed of environment change when combined with online update. Experiment shows it works very well with energy features in both stationary and non-stationary environments.

Fuzzy clustering and Bayesian information criterion based threshold estimation for robust voice activity detection

A new scheme for nonspeech rejection is proposed by considering that most nonspeech segments do not have well-defined prosodic structures as speech segments do. Certain parameters characterizing the smoothness of the peak index series and of the peak amplitude series of the normalized autocorrelation function are used to make nonspeech segment rejection decisions. The receiver-operating-characteristics curve and recognition word-error-rate reduction measures show that our approach is more effective than garbage-model-based schemes when used in telephone speech recognition.

Nonspeech segment rejection based on prosodic information for robust speech recognition

Projection Approximation Subspace Tracking (PAST) algorithm gives biased subspace estimation when the received signal is corrupted by colored noise. In this paper, an unbiased version of PAST is proposed for the colored noise scenario. Firstly, a maximum likelihood (ML) and minimum variance unbiased (MVUB) estimator for the clean signal is derived using simultaneous diagonalization and oblique projection. Then, we provide a recursive algorithm, named Oblique PAST (obPAST), to track the signal subspace and update the estimator in colored noise. Experimental results show the effectiveness of the obPAST algorithm.

Subspace Tracking in Colored Noise Based on Oblique Projection

In this paper, a new microphone array speech recognition system in which the array processor and the speech recognizer are closely coupled is studied. The system includes a generalized sidelobe canceller (GSC) beamformer followed by a recognizer with vector Taylor series (VTS) compensation. The GSC beamformer provides two outputs, allowing more information to be used in the recognizer. One is the enhanced target speech output, the other is the reference noise output. VTS is used to compensate the effect of the residual noise in the GSC speech output, utilizing the GSC reference noise output. The compensation is done in a minimum mean square error (MMSE) sense. Moreover, an iteration procedure using an expectation-maximization (EM) algorithm is developed to refine the compensation parameters. Experimental results on the MONC database showed that the new system significantly improved the speech recognition performance in overlapping speech situations.

/pdf/closely-coupled-array-processing-and-model-based-3b7cpxczuw.pdf

Closely coupled array processing and model-based compensation for microphone array speech recognition

A speaker adaptation scheme named maximum likelihood model interpolation (MLMI) is proposed. The basic idea of MLMI is to compute the speaker adapted (SA) model of a test speaker by a linear convex combination of a set of speaker dependent (SD) models. Given a set of training speakers, we first calculate the corresponding SD models for each training speaker as well as the speaker-independent (SI) models. Then, the mean vector of the SA model is computed as the weighted sum of the set of the SD mean vectors, while the covariance matrix is the same as that of the SI model. An algorithm to estimate the weight parameters is given which maximizes the likelihood of the SA model given the adaptation data. Experiments show that 3 adaptation sentences can give a significant performance improvement. As the number of SD models increases, further improvement can be obtained.

Speaker adaptation using maximum likelihood model interpolation

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