Objective and Subjective Evaluation of Wideband Speech Quality

Traditional landline and cellular communications use a bandwidth of 300 - 3400 Hz for transmitting speech. This narrow bandwidth impacts quality, intelligibility and naturalness of transmitted speech. There is an impending change within the telecommunication industry towards using wider bandwidth speech, but the enlarged bandwidth also introduces a few challenges in speech processing. Echo and noise are two challenging issues in wideband telephony, due to increased perceptual sensitivity by users. Subjective and/or objective measurements of speech quality are important in benchmarking speech processing algorithms and evaluating the effect of parameters like noise, echo, and delay in wideband telephony. Subjective measures include ratings of speech quality by listeners, whereas objective measures compute a metric based on the reference and degraded speech samples. While subjective quality ratings are the gold - standard\u27\u27, they are also time- and resource- consuming. An objective metric that correlates highly with subjective data is attractive, as it can act as a substitute for subjective quality scores in gauging the performance of different algorithms and devices. This thesis reports results from a series of experiments on subjective and objective speech quality evaluation for wideband telephony applications. First, a custom wideband noise reduction database was created that contained speech samples corrupted by different background noises at different signal to noise ratios (SNRs) and processed by six different noise reduction algorithms. Comprehensive subjective evaluation of this database revealed an interaction between the algorithm performance, noise type and SNR. Several auditory-based objective metrics such as the Loudness Pattern Distortion (LPD) measure based on the Moore - Glasberg auditory model were evaluated in predicting the subjective scores. In addition, the performance of Bayesian Multivariate Regression Splines(BMLS) was also evaluated in terms of mapping the scores calculated by the objective metrics to the true quality scores. The combination of LPD and BMLS resulted in high correlation with the subjective scores and was used as a substitution for fine - tuning the noise reduction algorithms. Second, the effect of echo and delay on the wideband speech was evaluated in both listening and conversational context, through both subjective and objective measures. A database containing speech samples corrupted by echo with different delay and frequency response characteristics was created, and was later used to collect subjective quality ratings. The LPD - BMLS objective metric was then validated using the subjective scores. Third, to evaluate the effect of echo and delay in conversational context, a realtime simulator was developed. Pairs of subjects conversed over the simulated system and rated the quality of their conversations which were degraded by different amount of echo and delay. The quality scores were analysed and LPD+BMLS combination was found to be effective in predicting subjective impressions of quality for condition-averaged data

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Contribution to quality of user experience provision over wireless networks

The widespread expansion of wireless networks has brought new attractive possibilities to end users. In addition to the mobility capabilities provided by unwired devices, it is worth remarking the easy configuration process that a user has to follow to gain connectivity through a wireless network. Furthermore, the increasing bandwidth provided by the IEEE 802.11 family has made possible accessing to high-demanding services such as multimedia communications. Multimedia traffic has unique characteristics that make it greatly vulnerable against network impairments, such as packet losses, delay, or jitter. Voice over IP (VoIP) communications, video-conference, video-streaming, etc., are examples of these high-demanding services that need to meet very strict requirements in order to be served with acceptable levels of quality. Accomplishing these tough requirements will become extremely important during the next years, taking into account that consumer video traffic will be the predominant traffic in the Internet during the next years. In wired systems, these requirements are achieved by using Quality of Service (QoS) techniques, such as Differentiated Services (DiffServ), traffic engineering, etc. However, employing these methodologies in wireless networks is not that simple as many other factors impact on the quality of the provided service, e.g., fading, interferences, etc. Focusing on the IEEE 802.11g standard, which is the most extended technology for Wireless Local Area Networks (WLANs), it defines two different architecture schemes. On one hand, the infrastructure mode consists of a central point, which manages the network, assuming network controlling tasks such as IP assignment, routing, accessing security, etc. The rest of the nodes composing the network act as hosts, i.e., they send and receive traffic through the central point. On the other hand, the IEEE 802.11 ad-hoc configuration mode is less extended than the infrastructure one. Under this scheme, there is not a central point in the network, but all the nodes composing the network assume both host and router roles, which permits the quick deployment of a network without a pre-existent infrastructure. This type of networks, so called Mobile Ad-hoc NETworks (MANETs), presents interesting characteristics for situations when the fast deployment of a communication system is needed, e.g., tactics networks, disaster events, or temporary networks. The benefits provided by MANETs are varied, including high mobility possibilities provided to the nodes, network coverage extension, or network reliability avoiding single points of failure. The dynamic nature of these networks makes the nodes to react to topology changes as fast as possible. Moreover, as aforementioned, the transmission of multimedia traffic entails real-time constraints, necessary to provide these services with acceptable levels of quality. For those reasons, efficient routing protocols are needed, capable of providing enough reliability to the network and with the minimum impact to the quality of the service flowing through the nodes. Regarding quality measurements, the current trend is estimating what the end user actually perceives when consuming the service. This paradigm is called Quality of user Experience (QoE) and differs from the traditional Quality of Service (QoS) approach in the human perspective given to quality estimations. In order to measure the subjective opinion that a user has about a given service, different approaches can be taken. The most accurate methodology is performing subjective tests in which a panel of human testers rates the quality of the service under evaluation. This approach returns a quality score, so-called Mean Opinion Score (MOS), for the considered service in a scale 1 - 5. This methodology presents several drawbacks such as its high expenses and the impossibility of performing tests at real time. For those reasons, several mathematical models have been presented in order to provide an estimation of the QoE (MOS) reached by different multimedia services In this thesis, the focus is on evaluating and understanding the multimedia-content transmission-process in wireless networks from a QoE perspective. To this end, firstly, the QoE paradigm is explored aiming at understanding how to evaluate the quality of a given multimedia service. Then, the influence of the impairments introduced by the wireless transmission channel on the multimedia communications is analyzed. Besides, the functioning of different WLAN schemes in order to test their suitability to support highly demanding traffic such as the multimedia transmission is evaluated. Finally, as the main contribution of this thesis, new mechanisms or strategies to improve the quality of multimedia services distributed over IEEE 802.11 networks are presented. Concretely, the distribution of multimedia services over ad-hoc networks is deeply studied. Thus, a novel opportunistic routing protocol, so-called JOKER (auto-adJustable Opportunistic acK/timEr-based Routing) is presented. This proposal permits better support to multimedia services while reducing the energy consumption in comparison with the standard ad-hoc routing protocols.Universidad Politécnica de CartagenaPrograma Oficial de Doctorado en Tecnologías de la Información y Comunicacione

Electroacoustic and Behavioural Evaluation of Hearing Aid Digital Signal Processing Features

Modern digital hearing aids provide an array of features to improve the user listening experience. As the features become more advanced and interdependent, it becomes increasingly necessary to develop accurate and cost-effective methods to evaluate their performance. Subjective experiments are an accurate method to determine hearing aid performance but they come with a high monetary and time cost. Four studies that develop and evaluate electroacoustic hearing aid feature evaluation techniques are presented. The first study applies a recent speech quality metric to two bilateral wireless hearing aids with various features enabled in a variety of environmental conditions. The study shows that accurate speech quality predictions are made with a reduced version of the original metric, and that a portion of the original metric does not perform well when applied to a novel subjective speech quality rating database. The second study presents a reference free (non-intrusive) electroacoustic speech quality metric developed specifically for hearing aid applications and compares its performance to a recent intrusive metric. The non-intrusive metric offers the advantage of eliminating the need for a shaped reference signal and can be used in real time applications but requires a sacrifice in prediction accuracy. The third study investigates the digital noise reduction performance of seven recent hearing aid models. An electroacoustic measurement system is presented that allows the noise and speech signals to be separated from hearing aid recordings. It is shown how this can be used to investigate digital noise reduction performance through the application of speech quality and speech intelligibility measures. It is also shown how the system can be used to quantify digital noise reduction attack times. The fourth study presents a turntable-based system to investigate hearing aid directionality performance. Two methods to extract the signal of interest are described. Polar plots are presented for a number of hearing aid models from recordings generated in both the free-field and from a head-and-torso simulator. It is expected that the proposed electroacoustic techniques will assist Audiologists and hearing researchers in choosing, benchmarking, and fine-tuning hearing aid features

Glottal-synchronous speech processing

Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity of voiced speech is exploited. Traditionally, speech processing involves segmenting and processing short speech frames of predefined length; this may fail to exploit the inherent periodic structure of voiced speech which glottal-synchronous speech frames have the potential to harness. Glottal-synchronous frames are often derived from the glottal closure instants (GCIs) and glottal opening instants (GOIs). The SIGMA algorithm was developed for the detection of GCIs and GOIs from the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and GOI detection from speech signals, the YAGA algorithm provides a measured accuracy of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to reverberation than single-channel algorithms. The GCIs are applied to real-world applications including speech dereverberation, where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance of voicing detection in glottal-synchronous algorithms is demonstrated by subjective testing. The GCIs are further exploited in a new area of data-driven speech modelling, providing new insights into speech production and a set of tools to aid deployment into real-world applications. The technique is shown to be applicable in areas of speech coding, identification and artificial bandwidth extension of telephone speec

Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids

Speech enhancement in assistive hearing devices has been an area of research for many decades. Noise reduction is particularly challenging because of the wide variety of noise sources and the non-stationarity of speech and noise. Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired signals. This could be disadvantageous in accurate estimation of different noise types, which subsequently leads to suboptimal noise reduction. In this research, a relatively unexplored technique based on deep learning, i.e. Recurrent Neural Network (RNN), is used to perform noise reduction and dereverberation for assisting hearing-impaired listeners. For noise reduction, the performance of the deep learning model was evaluated objectively and compared with that of open Master Hearing Aid (openMHA), a conventional signal processing based framework, and a Deep Neural Network (DNN) based model. It was found that the RNN model can suppress noise and improve speech understanding better than the conventional hearing aid noise reduction algorithm and the DNN model. The same RNN model was shown to reduce reverberation components with proper training. A real-time implementation of the deep learning model is also discussed

Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss

Listeners with hearing loss often struggle to understand speech in noise, even with a hearing aid. To better understand the auditory processing deficits that underlie this problem, we made large-scale brain recordings from gerbils, a common animal model for human hearing, while presenting a large database of speech and noise sounds. We first used manifold learning to identify the neural subspace in which speech is encoded and found that it is low-dimensional and that the dynamics within it are profoundly distorted by hearing loss. We then trained a deep neural network (DNN) to replicate the neural coding of speech with and without hearing loss and analyzed the underlying network dynamics. We found that hearing loss primarily impacts spectral processing, creating nonlinear distortions in cross-frequency interactions that result in a hypersensitivity to background noise that persists even after amplification with a hearing aid. Our results identify a new focus for efforts to design improved hearing aids and demonstrate the power of DNNs as a tool for the study of central brain structures

Performance Analysis of Advanced Front Ends on the Aurora Large Vocabulary Evaluation

Over the past few years, speech recognition technology performance on tasks ranging from isolated digit recognition to conversational speech has dramatically improved. Performance on limited recognition tasks in noiseree environments is comparable to that achieved by human transcribers. This advancement in automatic speech recognition technology along with an increase in the compute power of mobile devices, standardization of communication protocols, and the explosion in the popularity of the mobile devices, has created an interest in flexible voice interfaces for mobile devices. However, speech recognition performance degrades dramatically in mobile environments which are inherently noisy. In the recent past, a great amount of effort has been spent on the development of front ends based on advanced noise robust approaches. The primary objective of this thesis was to analyze the performance of two advanced front ends, referred to as the QIO and MFA front ends, on a speech recognition task based on the Wall Street Journal database. Though the advanced front ends are shown to achieve a significant improvement over an industry-standard baseline front end, this improvement is not operationally significant. Further, we show that the results of this evaluation were not significantly impacted by suboptimal recognition system parameter settings. Without any front end-specific tuning, the MFA front end outperforms the QIO front end by 9.6% relative. With tuning, the relative performance gap increases to 15.8%. Finally, we also show that mismatched microphone and additive noise evaluation conditions resulted in a significant degradation in performance for both front ends