An Efficient Reference Free Adaptive Learning Process for Speech Enhancement Applications

Abstract

In issues like hearing impairment, speech therapy and hearing aids play a major role in reducing the impairment. Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy. During the transmission of speech signals, several noise components contaminate the actual speech components. This paper addresses a new adaptive speech enhancement (ASE) method based on a modified version of singular spectrum analysis (MSSA). The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component. The MSSA adopts three key steps for generating the reference from the contaminated speech only. These are decomposition, grouping and reconstruction. The generated reference is taken as a reference for variable size adaptive learning algorithms. In this work two categories of adaptive learning algorithms are used. They are step variable adaptive learning (SVAL) algorithm and time variable step size adaptive learning (TVAL). Further, sign regressor function is applied to adaptive learning algorithms to reduce the computational complexity of the proposed adaptive learning algorithms. The performance measures of the proposed schemes are calculated in terms of signal to noise ratio improvement (SNRI), excess mean square error (EMSE) and misadjustment (MSD). For cockpit noise these measures are found to be 29.2850, -27.6060 and 0.0758 dB respectively during the experiments using SVAL algorithm. By considering the reduced number of multiplications the sign regressor version of SVAL based ASE method is found to better then the counter parts.

Research on Tibetan Speech Recognition Based on the Am-do Dialect

“…This research trained 5 triphone models. The first-order difference and secondorder difference information of the acoustic features were added and recorded as the tri1 model; linear discriminant analysis (LDA) and maximum likelihood linear regression (MLLR) were used as two linear transformations for the features to create the tri2 model; speaker adaptation was added to the tri3 model (using LDA, MLLT + SAT); the tri4 model built a larger SAT model by adjusting parameters; and finally, the "quick fast training" script in Kaldi was used to train a larger scale GMM model, denoted the tri5p model [21][22][23].…”

Speech Enhancement via Mask-Mapping Based Residual Dense Network

“…Speech enhancement is a fundamental task in speech signal processing, which is widely used in various scenarios, e.g., mobile phone, intelligent vehicles [1] and medical devices [2,3]. It is performed as a front-end signal procedure for automatic speech recognition (ASR), speaker identification, hearingaid devices and cochlear implant.…”

Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

“…In practical application scenarios, speech signals will inevitably be disturbed by many interference factors such as noise, echo, and reverberation. Therefore, speech enhancement technology has been widely used in household appliances, communications, speech recognition, automotive electronics, hearing aids, and other fields [1][2][3]. Traditional speech enhancement methods, based on signal processing and statistical modeling, have good performance for stationary noise.…”