Noise Robust Keyword Spotting Using Deep Neural Networks For Embedded Platforms

The recent development of embedded platforms along with spectacular growth in communication networking technologies is driving the Internet of things to thrive. More complex tasks are now possible to operate in small devices such as speech recognition and keyword spotting which are in great demand. Traditional voice recognition approaches are already being used in several embedded applications, some are hybrid(cloud-based and embedded) while others are fully embedded. However, the environment surrounding the embedded devices is usually accompanied by noise. Conventional approaches to add noise robustness to speech recognition are effective but also costly in terms of memory consumption and hardware complexities which limit their use in embedded platforms. The purpose of this thesis is to increase the robustness of keyword spotting to more than one type of noise at once without increasing the memory footprint or the need for a denoiser while maintaining the recognition accuracy to an acceptable level. In this work, robustness in treated at the phoneme classification level as the phoneme based keyword spotting is the best technique for embedded keyword spotting. Deep Neural Networks have been successfully deployed in many applications including noise robust speech recognition. In this work, we use mutil-condition utterances training of a Deep Neural Networks model to increase the keyword spotting noise robustness. This technique is also used for a Gaussian mixture model training. The two approaches are compared and the deep learning proved to not only outperform the Gaussian approach, but has also outperformed the use of a denoiser system. This results in a smaller, more accurate and noise robust model for phoneme recognition

Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation

This paper considers estimation of the noise spectral variance from speech signals contaminated by highly nonstationary noise sources. The method can accurately track fast changes in noise power level (up to about 10 dB/s). In each time frame, for each frequency bin, the noise variance estimate is updated recursively with the minimum mean-square error (mmse) estimate of the current noise power. A time- and frequency-dependent smoothing parameter is used, which is varied according to an estimate of speech presence probability. In this way, the amount of speech power leaking into the noise estimates is kept low. For the estimation of the noise power, a spectral gain function is used, which is found by an iterative data-driven training method. The proposed noise tracking method is tested on various stationary and nonstationary noise sources, for a wide range of signal-to-noise ratios, and compared with two state-of-the-art methods. When used in a speech enhancement system, improvements in segmental signal-to-noise ratio of more than 1 dB can be obtained for the most nonstationary noise sources at high noise levels.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Methods of Optimizing Speech Enhancement for Hearing Applications

Speech intelligibility in hearing applications suffers from background noise. One of the most effective solutions is to develop speech enhancement algorithms based on the biological traits of the auditory system. In humans, the medial olivocochlear (MOC) reflex, which is an auditory neural feedback loop, increases signal-in-noise detection by suppressing cochlear response to noise. The time constant is one of the key attributes of the MOC reflex as it regulates the variation of suppression over time. Different time constants have been measured in nonhuman mammalian and human auditory systems. Physiological studies reported that the time constant of nonhuman mammalian MOC reflex varies with the properties (e.g. frequency, bandwidth) changes of the stimulation. A human based study suggests that time constant could vary when the bandwidth of the noise is changed. Previous works have developed MOC reflex models and successfully demonstrated the benefits of simulating the MOC reflex for speech-in-noise recognition. However, they often used fixed time constants. The effect of the different time constants on speech perception remains unclear. The main objectives of the present study are (1) to study the effect of the MOC reflex time constant on speech perception in different noise conditions; (2) to develop a speech enhancement algorithm with dynamic time constant optimization to adapt to varying noise conditions for improving speech intelligibility. The first part of this thesis studies the effect of the MOC reflex time constants on speech-in-noise perception. Conventional studies do not consider the relationship between the time constants and speech perception as it is difficult to measure the speech intelligibility changes due to varying time constants in human subjects. We use a model to investigate the relationship by incorporating Meddis’ peripheral auditory model (which includes a MOC reflex) with an automatic speech recognition (ASR) system. The effect of the MOC reflex time constant is studied by adjusting the time constant parameter of the model and testing the speech recognition accuracy of the ASR. Different time constants derived from human data are evaluated in both speech-like and non-speech like noise at the SNR levels from -10 dB to 20 dB and clean speech condition. The results show that the long time constants (≥1000 ms) provide a greater improvement of speech recognition accuracy at SNR levels≤10 dB. Maximum accuracy improvement of 40% (compared to no MOC condition) is shown in pink noise at the SNR of 10 dB. Short time constants (<1000 ms) show recognition accuracy over 5% higher than the longer ones at SNR levels ≥15 dB. The second part of the thesis develops a novel speech enhancement algorithm based on the MOC reflex with a time constant that is dynamically optimized, according to a lookup table for varying SNRs. The main contributions of this part include: (1) So far, the existing SNR estimation methods are challenged in cases of low SNR, nonstationary noise, and computational complexity. High computational complexity would increase processing delay that causes intelligibility degradation. A variance of spectral entropy (VSE) based SNR estimation method is developed as entropy based features have been shown to be more robust in the cases of low SNR and nonstationary noise. The SNR is estimated according to the estimated VSE-SNR relationship functions by measuring VSE of noisy speech. Our proposed method has an accuracy of 5 dB higher than other methods especially in the babble noise with fewer talkers (2 talkers) and low SNR levels (< 0 dB), with averaging processing time only about 30% of the noise power estimation based method. The proposed SNR estimation method is further improved by implementing a nonlinear filter-bank. The compression of the nonlinear filter-bank is shown to increase the stability of the relationship functions. As a result, the accuracy is improved by up to 2 dB in all types of tested noise. (2) A modification of Meddis’ MOC reflex model with a time constant dynamically optimized against varying SNRs is developed. The model incudes simulated inner hair cell response to reduce the model complexity, and now includes the SNR estimation method. Previous MOC reflex models often have fixed time constants that do not adapt to varying noise conditions, whilst our modified MOC reflex model has a time constant dynamically optimized according to the estimated SNRs. The results show a speech recognition accuracy of 8 % higher than the model using a fixed time constant of 2000 ms in different types of noise. (3) A speech enhancement algorithm is developed based on the modified MOC reflex model and implemented in an existing hearing aid system. The performance is evaluated by measuring the objective speech intelligibility metric of processed noisy speech. In different types of noise, the proposed algorithm increases intelligibility at least 20% in comparison to unprocessed noisy speech at SNRs between 0 dB and 20 dB, and over 15 % in comparison to processed noisy speech using the original MOC based algorithm in the hearing aid