Acoustics Recognition with Expert Intelligent System

International Journal of Speech Technology, 2020

Bayesian Estimators are very useful in speech enhancement and noise reduction. But, it is noted that the traditional estimators process only amplitudes and the phase is left unprocessed. Among the Bayesian estimators, Super-Gaussian based estimators provide improved noise reduction. Super-Gaussian Bayesian estimators, which uses processed phase information for estimation of amplitudes provides further improved results. In this work, the Complex speech coefficients given Uncertain Phase (CUP) based Bayesian estimators like CUP-GG (CUP Estimator with speech spectral coefficients assumed as Gamma and noise spectral coefficients as Generalized Gamma), CUP-NG (Speech as Nakagami) are compared under white noise, pink noise, Babble noise and Non-Stationary factory noise conditions. The statistical estimators show less effective results under completely non-stationary assumptions like non-stationary factory noise, babble noise etc. Non-negative Matrix Factorization (NMF) based algorithms show better performance for non stationary noises. The drawback of NMF is, it requires apriori knowledge about speech. This drawback can be overcome by taking the advantages of both statistical approaches and NMF approaches. NR-NMF and WR-NMF speech enhancement methods are developed by providing posteriori regularization based on statistical assumption of speech and noise DFT coefficients distribution. Also a speech enhancement method which uses CUP-GG estimator and NMF with online noise bases update are considered for comparison. The progress in neural network based approaches for speech enhancement further shown that with large dataset and better training, the speech enhancement algorithms results in improved results. In this work, the neural network approach for speech enhancement is implemented and compared the method with traditional estimators and NMF approaches. For generalization of unseen noise types the proposed neural network approach uses dropout. Also for training the network, the features obtained from apriori SNR and aposteriori SNR is used in this method. The objective of this paper is to analyze the performance of speech enhancement methods based on Neural Network, NMF and statistical based. The objective performance measures Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), Signal to Noise Ratio (SNR), Segmental SNR (Seg SNR) are considered for comparison.

2021

This paper introduces an evolutionary approach for training the adaptive network-based fuzzy inference system (ANFIS). The previous works are based on gradient descendent (GD); this algorithm converges very slowly and gets stuck down at bad local minima. This study applies one of the swarm intelligent branches, named particle swarm optimization (PSO), where the premise parameters of the rules are optimized by a PSO, and the conclusion part is optimized by least-squares estimation (LSE). The hybrid PSO-ANFIS model is performed for speaker recognition on CHAINS speech dataset. The results obtained by the hybrid model showed an improvement on the accuracy compared to similar ANFIS based on gradient descendent optimization.

It is a well known fact that, speech recognition systems perform well when the system is used in conditions similar to the one used to train the acoustic models. However, mismatches degrade the performance. In adverse environment, it is very difficult to predict the category of noise in advance in case of real world environmental noise and difficult to achieve environmental robustness. After doing rigorous experimental study it is observed that, a unique method is not available that will clean the noisy speech as well as preserve the quality which have been corrupted by real natural environmental (mixed) noise. It is also observed that only back-end techniques are not sufficient to improve the performance of a speech recognition system. It is necessary to implement performance improvement techniques at every step of back-end as well as front-end of the Automatic Speech Recognition (ASR) model. Current recognition systems solve this problem using a technique called adaptation. This study presents an experimental study that aims two points, first is to implement the hybrid method that will take care of clarifying the speech signal as much as possible with all combinations of filters and enhancement techniques. The second point is to develop a method for training all categories of noise that can adapt the acoustic models for a new environment that will help to improve the performance of the speech recognizer under real world environmental mismatched conditions. This experiment confirms that hybrid adaptation methods improve the ASR performance on both levels, (Signal-to-Noise Ratio) SNR improvement as well as word recognition accuracy in real world noisy environment.

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016

In this paper we present a single-microphone speech enhancement algorithm. A hybrid approach is proposed merging the generative Mixture of Gaussians (MoG) model and the discriminative neural network (NN). The proposed algorithm is executed in two phases, the training phase, which does not recur, and the test phase. First, the noise-free speech power spectral density (PSD) is modeled as a MoG, representing the phoneme based diversity in the speech signal. An NN is then trained with phoneme labeled database for phoneme classification with mel-frequency cepstral coefficients (MFCC) as the input features. Given the phoneme classification results, an speech presence probability (SPP) is obtained using both the generative and discriminative models. Soft spectral subtraction is then executed while simultaneously, the noise estimation is updated. The discriminative NN maintain the continuity of the speech and the generative phoneme-based MoG preserves the speech spectral structure. Extensive experimental study using real speech and noise signals is provided. We also compare the proposed algorithm with alternative speech enhancement algorithms. We show that we obtain a significant improvement over previous methods in terms of both speech quality measures and speech recognition results.

Bulletin of Electrical Engineering and Informatics, 2023

Speech quality significantly affects the performance of speech dependent systems. Noise in the background lowers the clarity and intelligibility of speech. The augmentation of speech can increase its quality. We propose a single-channel speech improvement framework that combines particle swarm optimization (PSO), gravitational search algorithm (GSA), and harmonic regeneration noise reduction (HRNR) to minimize speech signal noise and increase speech intelligibility. The proposed hybrid algorithm optimizes the amount of overlap between the noisy speech frames. This helps in reducing the overlapped noise. Then HRNR algorithm is applied to retain the speech harmonics. The algorithm gives improvement in the speech intelligibility for babble, car and exhibition noise. The segmental signal to noise ratio (SNR) is also improved for these noise types. There is improvement in speech intelligibility with minimal speech distortion.

2000

Speech and signal processing technologies need new methods that deal with the problems of noise and adaptation in order for these technologies to become common tools for communication and information processing. This paper is concerned with a method and a system for adaptive speech recognition in a noisy environment (ASN). A system based on the described method can store words and phrases spoken by the user and subsequently recognize them when they are pronounced as connected words in a noisy environment. The method guarantees system robustness in respect to noise, regardless of its origin and level. New words, pronunciations, and languages can be introduced to the system in an incremental, adaptive mode. The method and system are based on novel techniques recently created by the authors, namely: adaptive noise suppression, and evolving connectionist systems. Potential applications are numerous, e.g. voice dialing in a noisy environment, voice command control, improved wireless communications, data entry into databases, helping disabled people, multimedia systems, improved human computer interaction. The method and system are illustrated on the recognition of English and Italian spoken digits in different noisy environments.

2006

This paper shows a fuzzy logic speech/non-speech discrimination method for improving the performance of speech processing systems working in noise environments. The fuzzy system is based on a Sugeno inference engine with membership functions defined as combination of two Gaussian functions. The rule base consists of ten fuzzy if then statements defined in terms of the denoised subband signal-tonoise ratios (SNRs) and the zero crossing rates (ZCRs). Its operation is optimized by means of a hybrid training algorithm combining the leastsquares method and the backpropagation gradient descent method for training membership function parameters. The experiments conducted on the Spanish SpeechDat-Car database shows that the proposed method yields clear improvements over a set of standardized VADs for discontinuous transmission (DTX) and distributed speech recognition (DSR) and also over recently published VAD methods.

In this paper we present a single-microphone speech enhancement algorithm. A hybrid approach is proposed merging the generative Mixture of Gaussians (MoG) model and the discriminative deep neural network (DNN). The proposed algorithm is executed in two phases, the training phase, which does not recur, and the test phase. First, the noise-free speech logpower spectral density (PSD) is modeled as a MoG, representing the phoneme-based diversity in the speech signal. A DNN is then trained with phoneme labeled database of clean speech signals for phoneme classification with mel-frequency cepstral coefficients (MFCC) as the input features. In the test phase, a noisy utterance of an untrained speech is processed. Given the phoneme classification results of the noisy speech utterance, a speech presence probability (SPP) is obtained using both the generative and discriminative models. SPP-controlled attenuation is then applied to the noisy speech while simultaneously, the noise estimate is updated. The discriminative DNN maintains the continuity of the speech and the generative phoneme-based MoG preserves the speech spectral structure. Extensive experimental study using real speech and noise signals is provided. We also compare the proposed algorithm with alternative speech enhancement algorithms. We show that we obtain a significant improvement over previous methods in terms of speech quality measures. Finally, we analyze the contribution of all components of the proposed algorithm indicating their combined importance.

Science and engineering journal, 2024

peech communicated is adversely affected by environmental noise. It is important to process the speech and reduce noise for better understanding. Speech enhancement or noise reduction is useful to provide comfort for human or machine listening. Traditional algorithms provide better noise reduction and better-quality speech. Due to the non-stationary nature of noise and the quasi-stationary nature of speech, the traditional methods are proven inadequate in achieving high-quality speech. Later statistical estimators based on Gaussian, and super-Gaussian Probability Density Function (PDF) assumption further improved the enhancement performance. But still, non-stationary noise nature introduces artifacts in processed signal and results in decreased performance. It is observed that neural network approaches and the factorization approach provide better performance even under non-stationary noises by proper training and large database. Different features Nomenclature n -Sample number l-Time frame k-frequency bin t-time 𝑥[𝑛]-Clean Speech Signal 𝑣[𝑛]-Noise Signal y[n]-Noisy Speech Signal h(t) -Convolutive Noise s(t)-Clean Speech 𝑌(𝑘, 𝑙) -Short time fourier transform coefficients of Noisy Speech Signal 𝑋(𝑘, 𝑙) -Short time fourier transform coefficients of Clean Speech Signal 𝑁(𝑘, 𝑙) -Short time fourier transform coefficients of Noise Signal. 𝑆 0 (𝜔) -Enhanced clean speech signal in frequency domain 𝐻 0 (𝜔) -Transfer Function/ Gain

International Journal of Interactive Multimedia and Artificial Intelligence, 2019

Many forms of human communication exist; for instance, text and nonverbal based. Speech is, however, the most powerful and dexterous form for the humans. Speech signals enable humans to communicate and this usefulness of the speech signals has led to a variety of speech processing applications. Successful use of these applications is, however, significantly aggravated in presence of the background noise distortions. These noise signals overlap and mask the target speech signals. To deal with these overlapping background noise distortions, a speech enhancement algorithm at front end is crucial in order to make noisy speech intelligible and pleasant. Speech enhancement has become a very important research and engineering problem for the last couple of decades. In this paper, we present an all-inclusive survey on unsupervised single-channel speech enhancement (U-SCSE) algorithms. A taxonomy based review of the U-SCSE algorithms is presented and the associated studies regarding improving the intelligibility and quality are outlined. The studies on the speech enhancement algorithms in unsupervised perspective are presented. Objective experiments have been performed to evaluate the potential of the U-SCSE algorithms in terms of improving the speech intelligibility and quality. It is found that unsupervised speech enhancement improves the speech quality but the speech intelligibility improvement is deprived. To finish, several research problems are identified that require further research.