Subspace-based Speech Enhancement Research Articles

A Hybrid Speech Enhancement Algorithm for Voice Assistance Application.

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.

Design and Implementation of Subspace-Based Speech Enhancement Under In-Car Noisy Environments

In this paper, a new subspace-based speech enhancement model is presented for in-car speech enhancement. To effectively suppress background noise, this model incorporates a perceptual filterbank and an auditory gain adaptation derived from a psychoacoustic model into a signal subspace approach. The projection approximation subspace tracking deflation (PASTd) algorithm is used to track the signal subspace. For real-time processing, a system-on-a-programmable-chip architecture and a very large scale integration design of the PASTd algorithm are proposed. To realize a pipeline computation, this paper presents a pipelined PASTd architecture without data-dependent hazards. The maximum clock rate is 9.7 MHz, and the typical clock rate, which achieves the real-time requirement, is 4.6 MHz. The corresponding architecture was experimentally verified via an ALTERA EPXA10 development board.

Critical Band Subspace-Based Speech Enhancement Using SNR and Auditory Masking Aware Technique

In this paper, a new subspace-based speech enhancement algorithm is presented. First, we construct a perceptual filterbank from psycho-acoustic model and incorporate it in the subspace-based enhancement approach. This filterbank is created through a five-level wavelet packet decomposition. The masking properties of the human auditory system are then derived based on the perceptual filterbank. Finally, the prior SNR and the masking threshold of each critical band are taken to decide the attenuation factor of the optimal linear estimator. Five different types of in-car noises in TAICAR database were used in our evaluation. The experimental results demonstrated that our approach outperformed conventional subspace and spectral subtraction methods.

A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition

The objective of this paper is threefold: (1) to provide an extensive review of signal subspace speech enhancement, (2) to derive an upper bound for the performance of these techniques, and (3) to present a comprehensive study of the potential of subspace filtering to increase the robustness of automatic speech recognisers against stationary additive noise distortions. Subspace filtering methods are based on the orthogonal decomposition of the noisy speech observation space into a signal subspace and a noise subspace. This decomposition is possible under the assumption of a low-rank model for speech, and on the availability of an estimate of the noise correlation matrix. We present an extensive overview of the available estimators, and derive a theoretical estimator to experimentally assess an upper bound to the performance that can be achieved by any subspace-based method. Automatic speech recognition (ASR) experiments with noisy data demonstrate that subspace-based speech enhancement can significantly increase the robustness of these systems in additive coloured noise environments. Optimal performance is obtained only if no explicit rank reduction of the noisy Hankel matrix is performed. Although this strategy might increase the level of the residual noise, it reduces the risk of removing essential signal information for the recogniser's back end. Finally, it is also shown that subspace filtering compares favourably to the well-known spectral subtraction technique.

Multiband subspace tracking speech enhancement for in-car human computer speech interaction

In this paper, a new subspace-based speech enhancement algorithm for in-car human computer speech interaction is presented. We first incorporate a perceptual filterbank which is derived from psycho-acoustic model with signal subspace approach to effectively suppress in-car noises of engine. Second, for real-time applications, a new subspace tracking algorithm is derived by modifying PASTd algorithm to solve the data dependent hazard of tacking algorithm. Six different types of in-car noises in TAICAR database are used in our evaluation. The experimental results demonstrate that our approach is superior to conventional subspace and spectral subtraction methods.

A perceptually motivated approach for speech enhancement

A new perceptually motivated approach is proposed for enhancement of speech corrupted by colored noise. The proposed approach takes into account the frequency masking properties of the human auditory system and reduces the perceptual effect of the residual noise. This new perceptual method is incorporated into a frequency-domain speech enhancement method and a subspace-based speech enhancement method. A better power spectrum/autocorrelation function estimator was also developed to improve the performance of the proposed algorithms. Objective measures and informal listening tests demonstrated significant improvements over other methods when tested with TIMIT sentences corrupted by various types of colored noise.

Efficient, high performance, subspace tracking for time-domain data

This paper describes two new algorithms for tracking the subspace spanned by the principal eigenvectors of the correlation matrix associated with time-domain (i.e., time series) data. The algorithms track the d principal N-dimensional eigenvectors of the data covariance matrix with a complexity of O(Nd/sup 2/), yet they have performance comparable with algorithms having O(N/sup 2/d) complexity. The computation reduction is achieved by exploiting the shift-invariance property of temporal data covariance matrices. Experiments are used to compare our algorithms with other well-known approaches of similar and/or lower complexity. Our new algorithms are shown to outperform the subset of the general approaches having the same complexity. The new algorithms are useful for applications such as subspace-based speech enhancement and low-rank adaptive filtering.