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There are many popular algorithms to recognize the human voice. The good algorithm not only results the high recognition accuracy, but also robust to noises. Several experiments are done in this research to verify the performance of the Neuro-fuzzy system to recognize the human voice. Eight words in Thai language recorded in a different environment, syllable and pronunciations are used as a data set...
A group of junior and senior researchers gathered as a part of the 2014 Frederick Jelinek Memorial Workshop in Prague to address the problem of predicting the accuracy of a nonlinear Deep Neural Network probability estimator for unknown data in a different application domain from the domain in which the estimator was trained. The paper describes the problem and summarizes approaches that were taken...
Traditional sound event recognition methods based on informative front end features such as MFCC, with back end sequencing methods such as HMM, tend to perform poorly in the presence of interfering acoustic noise. Since noise corruption may be unavoidable in practical situations, it is important to develop more robust features and classifiers. Recent advances in this field use powerful machine learning...
The presence of Lombard Effect in speech is proven to have severe effects on the performance of speech systems, especially speaker recognition. Varying kinds of Lombard speech are produced by speakers under influence of varying noise types [1]. This study proposes a high-accuracy classifier using deep neural networks for detecting various kinds of Lombard speech against neutral speech, independent...
Voice Activity Detection (VAD) is a very important front end processing in all Speech and Audio processing applications. The performance of most if not all speech/audio processing methods is crucially dependent on the performance of Voice Activity Detection. An ideal voice activity detector needs to be independent from application area and noise condition and have the least parameter tuning in real...
Human listeners are capable of recognizing speech in noisy environment, while most of the traditional speech recognition methods do not perform well in the presence of noise. Unlike traditional Mel-frequency cepstral coefficient (MFCC)-based method, this study proposes a phoneme classification technique using the neural responses of a physiologically-based computational model of the auditory periphery...
In this paper we address the problem of musical genre recognition for a dancing robot with embedded microphones capable of distinguishing the genre of a musical piece while moving in a real-world scenario. For this purpose, we assess and compare two state-of-the-art musical genre recognition systems, based on Support Vector Machines and Markov Models, in the context of different real-world acoustic...
Enhancement of speech distorted by reverberation is issue of the day. The problem has been actively studied in the last decade. However, it is still extremely difficult to find clear recommendations on choice of boundary value between early reflections and late reverberation, optimal in sense of such criteria as speech recognition accuracy and speech quality. Another problem is getting of simple pre-processor...
This paper addresses the problem of the automatic recognition of emotional states from speech recordings, especially those kind of emotions reflecting that the life or the human integrity are at risk. The paper compares the performance of two different systems: one being fed with speech signals recorded directly from the people (whole spectrum) and other one in which the speech signals are recorded...
This paper proposes a novel approach to enhance the speech features in noise robustness for speech recognition. In the proposed approach, the speech feature time sequence is first converted into the modulation spectral domain via discrete Fourier transform (DFT). The magnitude part of the modulation spectrum is decomposed into overlapped non-uniform sub-band segments, and then each sub-band segment...
In this paper, a noise robust formant frequency estimation scheme is developed utilizing the advantageous properties of the autocorrelation function of the band-limited noisy speech signal. It is shown that the use of autocorrelation operation on a speech signal, which is band-limited to a particular formant zone, in comparison to one without any band limitation, can provide higher noise immunity,...
This paper deals with a post-processing phase of automatic transcription of spoken documents stored in the large Czech Radio audio archive (containing hundreds of thousands of recordings). The ultimate goal of the project is to transcribe them and to allow public access to their content. In this paper we focus on methods and algorithms for unsupervised post-processing of automatically recognized recordings...
In this paper we propose Fourier-Bessel cepstral coefficients (FBCC) features for robust speech recognition. The Fourier-Bessel representation of the speech signal is obtained using Bessel function as a basis set. The FBCC are extracted from zeroth order Bessel coefficients taking into account of the perceptual characteristics of human auditory system. Recognition accuracy is measured using the CMU...
Phenomena like filled pauses, laughter, breathing, hesitation, etc. play significant role in everyday human-to-human conversation and have a significant influence on speech recognition accuracy [1]. Because of their nature (e. g. long duration), they should be modeled with different number of emitting states and Gaussian mixtures. In this paper we address this question and try to determine the most...
One of the most effective approaches to noise robust speech recognition is to remove the noise effect directly from corrupted MFCC vectors. However, VTS enhancement, which is a typical method for performing MFCC enhancement, provides limited improvement when the noise is highly non-stationary. This is because the VTS enhancement method cannot use a time-varying noise model to keep the computational...
This paper presents a new feature extraction algorithm called Power Normalized Cepstral Coefficients (PNCC) that is based on auditory processing. Major new features of PNCC processing include the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in MFCC coefficients, a noise-suppression algorithm based on asymmetric filtering that suppress background excitation, and...
Previous work has shown that spectro-temporal features reduce the word error rate for automatic speech recognition under noisy conditions. These systems, however, required significant hand-tuning in order to determine which spectral and temporal modulations should be included in a particular stream. In this work, streams are split into one spectral and temporal modulation each and their posterior...
Eigenvoice and vector Taylor series (VTS) are good models for speaker differences and environmental variations separately. However, speaker and environmental variation always coexist in real-world speech. In this paper, we propose to combine eigenvoice and VTS. Specifically, we introduce eigenvoice speaker modeling for the clean speech into VTS's nonlinear mismatch function. In contrast, the standard...
In this paper, speech recognition accuracy improvement is addressed for ICA-based multichannel noise reduction in spoken-dialogue robot. First, to achieve high recognition accuracy for the early utterance of the target speaker, we introduce a new rapid ICA initialization method combining robot image information and a prestored initial separation filter bank. From this image information, an ICA initial...
We propose Cross-Channel Spectral Subtraction (CCSS), a source separation method for recognizing meeting speech where one microphone is prepared for each speaker. The method quickly adapts to changes in transfer functions and uses spectral subtraction to suppress the speech of other speakers. Compared with conventional source separation methods based on independent component analysis (ICA) or that...
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