A data-driven approach that compensates the HMM parameters for the noisy speech recognition is proposed. Instead of assuming some statistical approximations as in the conventional methods such as the PMC, the various statistical information necessary for the HMM parameter adaptation is directly estimated by using the Baum-Welch algorithm. The proposed method has shown improved results compared ...

The statistical properties of a speech feature could differ under the influence of noisy environments. These effects are common in mismatched environments such as additive background noise and reverberant environments. Normalization strategies are employed in speech recognition systems to compensate for the effects of environmental mismatch. This work explores the utilization of cepstral moment...

Speech emotion recognition is mostly considered in clean speech. In this paper, joint spectro-temporal features (RS features) are extracted from an auditory model and are applied to detect the emotion status of noisy speech. The noisy speech is derived from the Berlin Emotional Speech database with added white and babble noises under various SNR levels. The clean train/noisy test scenario is in...

This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervised approach requires a very large amount of pair data for training, it is not...

In this work we extend a previously proposed NMF-based technique for speech enhancement of noisy speech to exploit a Hidden Markov Model (HMM). The NMF-based technique works by finding a sparse representation of specrogram segments of noisy speech in a dictionary containing both speech and noise exemplars, and uses the activated dictionary atoms to create a time-varying filter to enhance the no...

Monaural speech segregation is an important problem in robust speech processing and has been formulated as a supervised learning problem. In supervised learning methods, the ideal binary mask (IBM) is usually used as the target because of its simplicity and large speech intelligibility gains. Recently, the ideal ratio mask (IRM) has been found to improve the speech quality over the IBM. However...

In some field applications experts have to decode speech of poor quality. The speech material to decode is often degraded quite seriously and can’t be improved or changed to another, ‘clear’ copy. This report describes some principles of noisy speech recordings processing for improvement of message intelligibility and quality and some methods of speech enhancement for speech decoding. The discu...

This paper describes a new algorithm to enhance and recognise noisy speech when only the noisy signal is available. The system uses autoregressive hidden Markov models (HMMs) to model the clean speech and noise and combines these to form a model for the noisy speech. The combined model is used to determine the likelihood of each observation being just noise. These likelihoods are used to weight...