Learning static spectral weightings for speech intelligibility enhancement in noise

Near-end speech enhancement works by modifying speech prior to presentation in a noisy environment, typically operating under a constraint of limited or no increase in speech level. One issue is the extent to which near-end enhancement techniques require detailed estimates of the masking environment to function effectively. The current study investigated speech modification strategies based on reallocating energy statically across the spectrum using masker-specific spectral weightings. Weighting patterns were learned offline by maximising a glimpse-based objective intelligibility metric. Keyword scores in sentences in the presence of stationary and fluctuating maskers increased, in some cases by very substantial amounts, following the application of masker- and SNR-specific spectral weighting. A second experiment using generic masker-independent spectral weightings that boosted all frequencies above 1 kHz also led to significant gains in most conditions. These findings indicate that energy-neutral spectral weighting is a highly-effective near-end speech enhancement approach that places minimal demands on detailed masker estimation

Listeners’ Spectral Reallocation Preferences for Speech in Noise

Modifying the spectrum of recorded or synthetic speech is an effective strategy for boosting intelligibility in noise without increasing the speech level. However, the wider impact of changes to the spectral energy distribution of speech is poorly understood. The present study explored the influence of spectral modifications using an experimental paradigm in which listeners were able to adjust speech parameters directly with real-time audio feedback, allowing the joint elicitation of preferences and word recognition scores. In two experiments involving full-bandwidth and bandwidth-limited speech, respectively, listeners adjusted one of eight features that altered the speech spectrum, and then immediately carried out a sentence-in-noise recognition task at the chosen setting. Listeners’ preferred adjustments in most conditions involved the transfer of speech energy from the sub-1 kHz region to the 1–4 kHz range. Preferences were not random, even when intelligibility was at the ceiling or constant across a range of adjustment values, suggesting that listener choices encompass more than a desire to maintain comprehensibility.Olympia Simantiraki was funded by the European Commission under the Marie Curie European Training Network ENRICH (675324)

A study and experiment plan for digital mobile communication via satellite

The viability of mobile communications is examined within the context of a frequency division multiple access, single channel per carrier satellite system emphasizing digital techniques to serve a large population of users. The intent is to provide the mobile users with a grade of service consistant with the requirements for remote, rural (perhaps emergency) voice communications, but which approaches toll quality speech. A traffic model is derived on which to base the determination of the required maximum number of satellite channels to provide the anticipated level of service. Various voice digitalization and digital modulation schemes are reviewed along with a general link analysis of the mobile system. Demand assignment multiple access considerations and analysis tradeoffs are presented. Finally, a completed configuration is described

Minimum Processing Near-end Listening Enhancement

The intelligibility and quality of speech from a mobile phone or public announcement system are often affected by background noise in the listening environment. By pre-processing the speech signal it is possible to improve the speech intelligibility and quality -- this is known as near-end listening enhancement (NLE). Although, existing NLE techniques are able to greatly increase intelligibility in harsh noise environments, in favorable noise conditions the intelligibility of speech reaches a ceiling where it cannot be further enhanced. Actually, the focus of existing methods solely on improving the intelligibility causes unnecessary processing of the speech signal and leads to speech distortions and quality degradations. In this paper, we provide a new rationale for NLE, where the target speech is minimally processed in terms of a processing penalty, provided that a certain performance constraint, e.g., intelligibility, is satisfied. We present a closed-form solution for the case where the performance criterion is an intelligibility estimator based on the approximated speech intelligibility index and the processing penalty is the mean-square error between the processed and the clean speech. This produces an NLE method that adapts to changing noise conditions via a simple gain rule by limiting the processing to the minimum necessary to achieve a desired intelligibility, while at the same time focusing on quality in favorable noise situations by minimizing the amount of speech distortions. Through simulation studies, we show the proposed method attains speech quality on par or better than existing methods in both objective measurements and subjective listening tests, whilst still sustaining objective speech intelligibility performance on par with existing methods

Investigating supra-intelligibility aspects of speech

158 p.Synthetic and recorded speech form a great part of oureveryday listening experience, and much of our exposure tothese forms of speech occurs in potentially noisy settings such as on public transport, in the classroom or workplace, while driving, and in our homes. Optimising speech output to ensure that salient information is both correctly and effortlessly received is a main concern for the designers of applications that make use of the speech modality. Most of the focus in adapting speech output to challenging listening conditions has been on intelligibility, and specifically on enhancing intelligibility by modifying speech prior to presentation. However, the quality of the generated speech is not always satisfying for the recipient, which might lead to fatigue, or reluctance in using this communication modality. Consequently, a sole focus on intelligibility enhancement provides an incomplete picture of a listener¿s experience since the effect of modified or synthetic speech on other characteristics risks being ignored. These concerns motivate the study of 'supra-intelligibility' factors such as the additional cognitive demand that modified speech may well impose upon listeners, as well as quality, naturalness, distortion and pleasantness. This thesis reports on an investigation into two supra-intelligibility factors: listening effort and listener preferences. Differences in listening effort across four speech types (plain natural, Lombard, algorithmically-enhanced, and synthetic speech) were measured using existing methods, including pupillometry, subjective judgements, and intelligibility scores. To explore the effects of speech features on listener preferences, a new tool, SpeechAdjuster, was developed. SpeechAdjuster allows the manipulation of virtually any aspect of speech and supports the joint elicitation of listener preferences and intelligibility measures. The tool reverses the roles of listener and experimenter by allowing listeners direct control of speech characteristics in real-time. Several experiments to explore the effects of speech properties on listening preferences and intelligibility using SpeechAdjuster were conducted. Participants were permitted to change a speech feature during an open-ended adjustment phase, followed by a test phase in which they identified speech presented with the feature value selected at the end of the adjustment phase. Experiments with native normal-hearing listeners measured the consequences of allowing listeners to change speech rate, fundamental frequency, and other features which led to spectral energy redistribution. Speech stimuli were presented in both quiet and masked conditions. Results revealed that listeners prefer feature modifications similar to those observed in naturally modified speech in noise (Lombard speech). Further, Lombard speech required the least listening effort compared to either plain natural, algorithmically-enhanced, or synthetic speech. For stationary noise, as noise level increased listeners chose slower speech rates and flatter tilts compared to the original speech. Only the choice of fundamental frequency was not consistent with that observed in Lombard speech. It is possible that features such as fundamental frequency that talkers naturally modify are by-products of the speech type (e.g. hyperarticulated speech) and might not be advantageous for the listener.Findings suggest that listener preferences provide information about the processing of speech over and above that measured by intelligibility. One of the listeners¿ concerns was to maximise intelligibility. In noise, listeners preferred the feature values for which more information survived masking, choosing speech rates that led to a contrast with the modulation rate of the masker, or modifications that led to a shift of spectral energy concentration to higher frequencies compared to those of the masker. For all features being modified by listeners, preferences were evident even when intelligibility was at or close to ceiling levels. Such preferences might result from a desire to reduce the cognitive effort of understanding speech, or from a desire to reproduce the sound of typical speech features experienced in real-world noisy conditions, or to optimise the quality of the modified signal. Investigation of supra-intelligibility aspects of speech promises to improve the quality of speech enhancement algorithms, bringing with it the potential of reducing the effort of understanding artificially-modified or generated forms of speech

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

A robust speech enhancement method in noisy environments

Speech enhancement aims to eliminate or reduce undesirable noises and distortions, this processing should keep features of the speech to enhance the quality and intelligibility of degraded speech signals. In this study, we investigated a combined approach using single-frequency filtering (SFF) and a modified spectral subtraction method to enhance single-channel speech. The SFF method involves dividing the speech signal into uniform subband envelopes, and then performing spectral over-subtraction on each envelope. A smoothing parameter, determined by the a-posteriori signal-to-noise ratio (SNR), is used to estimate and update the noise without the need for explicitly detecting silence. To evaluate the performance of our algorithm, we employed objective measures such as segmental SNR (segSNR), extended short-term objective intelligibility (ESTOI), and perceptual evaluation of speech quality (PESQ). We tested our algorithm with various types of noise at different SNR levels and achieved results ranging from 4.24 to 15.41 for segSNR, 0.57 to 0.97 for ESTOI, and 2.18 to 4.45 for PESQ. Compared to other standard and existing speech enhancement methods, our algorithm produces better results and performs well in reducing undesirable noises

Speech Decomposition and Enhancement

The goal of this study is to investigate the roles of steady-state speech sounds and transitions between these sounds in the intelligibility of speech. The motivation for this approach is that the auditory system may be particularly sensitive to time-varying frequency edges, which in speech are produced primarily by transitions between vowels and consonants and within vowels. The possibility that selectively amplifying these edges may enhance speech intelligibility is examined. Computer algorithms to decompose speech into two different components were developed. One component, which is defined as a tonal component, was intended to predominately include formant activity. The second component, which is defined as a non-tonal component, was intended to predominately include transitions between and within formants.The approach to the decomposition is to use a set of time-varying filters whose center frequencies and bandwidths are controlled to identify the strongest formant components in speech. Each center frequency and bandwidth is estimated based on FM and AM information of each formant component. The tonal component is composed of the sum of the filter outputs. The non-tonal component is defined as the difference between the original speech signal and the tonal component.The relative energy and intelligibility of the tonal and non-tonal components were compared to the original speech. Psychoacoustic growth functions were used to assess the intelligibility. Most of the speech energy was in the tonal component, but this component had a significantly lower maximum word recognition than the original and non-tonal component had. The non-tonal component averaged 2% of the original speech energy, but this component had almost equal maximum word recognition as the original speech. The non-tonal component was amplified and recombined with the original speech to generate enhanced speech. The energy of the enhanced speech was adjusted to be equal to the original speech, and the intelligibility of the enhanced speech was compared to the original speech in background noise. The enhanced speech showed higher recognition scores at lower SNRs, and the differences were significant. The original and enhanced speech showed similar recognition scores at higher SNRs. These results suggest that amplification of transient information can enhance the speech in noise and this enhancement method is more effective at severe noise conditions