Model-Based Speech Enhancement

Abstract A method of speech enhancement is developed that reconstructs clean speech from a set of acoustic features using a harmonic plus noise model of speech. This is a significant departure from traditional filtering-based methods of speech enhancement. A major challenge with this approach is to estimate accurately the acoustic features (voicing, fundamental frequency, spectral envelope and phase) from noisy speech. This is achieved using maximum a-posteriori (MAP) estimation methods that operate on the noisy speech. In each case a prior model of the relationship between the noisy speech features and the estimated acoustic feature is required. These models are approximated using speaker-independent GMMs of the clean speech features that are adapted to speaker-dependent models using MAP adaptation and for noise using the Unscented Transform. Objective results are presented to optimise the proposed system and a set of subjective tests compare the approach with traditional enhancement methods. Threeway listening tests examining signal quality, background noise intrusiveness and overall quality show the proposed system to be highly robust to noise, performing significantly better than conventional methods of enhancement in terms of background noise intrusiveness. However, the proposed method is shown to reduce signal quality, with overall quality measured to be roughly equivalent to that of the Wiener filter

Model-Based Speech Enhancement in the Modulation Domain

This paper presents an algorithm for modulationdomain speech enhancement using a Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral amplitudes of speech and noise to obtain an MMSE estimation of the speech amplitude spectrum with the assumption that the speech and noise are additive in the complex domain. In order to include the dynamics of noise amplitudes with those of speech amplitudes, we propose a statistical “Gaussring” model that comprises a mixture of Gaussians whose centres lie in a circle on the complex plane. The performance of the proposed algorithm is evaluated using the perceptual evaluation of speech quality (PESQ) measure, segmental SNR (segSNR) measure and shorttime objective intelligibility (STOI) measure. For speech quality measures, the proposed algorithm is shown to give a consistent improvement over a wide range of SNRs when compared to competitive algorithms. Speech recognition experiments also show that the Gaussring model based algorithm performs well for two types of noise

Open-set Speaker Identification

This study is motivated by the growing need for effective extraction of intelligence and evidence from audio recordings in the fight against crime, a need made ever more apparent with the recent expansion of criminal and terrorist organisations. The main focus is to enhance open-set speaker identification process within the speaker identification systems, which are affected by noisy audio data obtained under uncontrolled environments such as in the street, in restaurants or other places of businesses. Consequently, two investigations are initially carried out including the effects of environmental noise on the accuracy of open-set speaker recognition, which thoroughly cover relevant conditions in the considered application areas, such as variable training data length, background noise and real world noise, and the effects of short and varied duration reference data in open-set speaker recognition. The investigations led to a novel method termed “vowel boosting” to enhance the reliability in speaker identification when operating with varied duration speech data under uncontrolled conditions. Vowels naturally contain more speaker specific information. Therefore, by emphasising this natural phenomenon in speech data, it enables better identification performance. The traditional state-of-the-art GMM-UBMs and i-vectors are used to evaluate “vowel boosting”. The proposed approach boosts the impact of the vowels on the speaker scores, which improves the recognition accuracy for the specific case of open-set identification with short and varied duration of speech material

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks