Intelligibility model optimisation approaches for speech pre-enhancement

The goal of improving the intelligibility of broadcast speech is being met by a recent new direction in speech enhancement: near-end intelligibility enhancement. In contrast to the conventional speech enhancement approach that processes the corrupted speech at the receiver-side of the communication chain, the near-end intelligibility enhancement approach pre-processes the clean speech at the transmitter-side, i.e. before it is played into the environmental noise. In this work, we describe an optimisation-based approach to near-end intelligibility enhancement using models of speech intelligibility to improve the intelligibility of speech in noise. This thesis first presents a survey of speech intelligibility models and how the adverse acoustic conditions affect the intelligibility of speech. The purpose of this survey is to identify models that we can adopt in the design of the pre-enhancement system. Then, we investigate the strategies humans use to increase speech intelligibility in noise. We then relate human strategies to existing algorithms for near-end intelligibility enhancement. A closed-loop feedback approach to near-end intelligibility enhancement is then introduced. In this framework, speech modifications are guided by a model of intelligibility. For the closed-loop system to work, we develop a simple spectral modification strategy that modifies the first few coefficients of an auditory cepstral representation such as to maximise an intelligibility measure. We experiment with two contrasting measures of objective intelligibility. The first, as a baseline, is an audibility measure named 'glimpse proportion' that is computed as the proportion of the spectro-temporal representation of the speech signal that is free from masking. We then propose a discriminative intelligibility model, building on the principles of missing data speech recognition, to model the likelihood of specific phonetic confusions that may occur when speech is presented in noise. The discriminative intelligibility measure is computed using a statistical model of speech from the speaker that is to be enhanced. Interim results showed that, unlike the glimpse proportion based system, the discriminative based system did not improve intelligibility. We investigated the reason behind that and we found that the discriminative based system was not able to target the phonetic confusion with the fixed spectral shaping. To address that, we introduce a time-varying spectral modification. We also propose to perform the optimisation on a segment-by-segment basis which enables a robust solution against the fluctuating noise. We further combine our system with a noise-independent enhancement technique, i.e. dynamic range compression. We found significant improvement in non-stationary noise condition, but no significant differences to the state-of-the art system (spectral shaping and dynamic range compression) where found in stationary noise condition

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

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

The Conversation: Deep Audio-Visual Speech Enhancement

Our goal is to isolate individual speakers from multi-talker simultaneous speech in videos. Existing works in this area have focussed on trying to separate utterances from known speakers in controlled environments. In this paper, we propose a deep audio-visual speech enhancement network that is able to separate a speaker's voice given lip regions in the corresponding video, by predicting both the magnitude and the phase of the target signal. The method is applicable to speakers unheard and unseen during training, and for unconstrained environments. We demonstrate strong quantitative and qualitative results, isolating extremely challenging real-world examples.Comment: To appear in Interspeech 2018. We provide supplementary material with interactive demonstrations on http://www.robots.ox.ac.uk/~vgg/demo/theconversatio

A study on the relationship between the intelligibility and quality of algorithmically-modified speech for normal hearing listeners

This study investigates the relationship between the intelligibility and quality of modified speech in noise and in quiet. Speech signals were processed by seven algorithms designed to increase speech intelligibility in noise without altering speech intensity. In three noise maskers, including both stationary and fluctuating noise at two signal-to-noise ratios (SNR), listeners identified keywords from unmodified or modified sentences. The intelligibility performance of each type of speech was measured as the listeners’ word recognition rate in each condition, while the quality was rated as a mean opinion score. In quiet, only the perceptual quality of each type of speech was assessed. The results suggest that when listening in noise, modification performance on improving intelligibility is more important than its potential negative impact on speech quality. However, when listening in quiet or at SNRs in which intelligibility is no longer an issue to listeners, the impact to speech quality due to modification becomes a concern

Speaker Re-identification with Speaker Dependent Speech Enhancement

While the use of deep neural networks has significantly boosted speaker recognition performance, it is still challenging to separate speakers in poor acoustic environments. Here speech enhancement methods have traditionally allowed improved performance. The recent works have shown that adapting speech enhancement can lead to further gains. This paper introduces a novel approach that cascades speech enhancement and speaker recognition. In the first step, a speaker embedding vector is generated , which is used in the second step to enhance the speech quality and re-identify the speakers. Models are trained in an integrated framework with joint optimisation. The proposed approach is evaluated using the Voxceleb1 dataset, which aims to assess speaker recognition in real world situations. In addition three types of noise at different signal-noise-ratios were added for this work. The obtained results show that the proposed approach using speaker dependent speech enhancement can yield better speaker recognition and speech enhancement performances than two baselines in various noise conditions.Comment: Acceptted for presentation at Interspeech202

Mask-based enhancement of very noisy speech

When speech is contaminated by high levels of additive noise, both its perceptual quality and its intelligibility are reduced. Studies show that conventional approaches to speech enhancement are able to improve quality but not intelligibility. However, in recent years, algorithms that estimate a time-frequency mask from noisy speech using a supervised machine learning approach and then apply this mask to the noisy speech have been shown to be capable of improving intelligibility. The most direct way of measuring intelligibility is to carry out listening tests with human test subjects. However, in situations where listening tests are impractical and where some additional uncertainty in the results is permissible, for example during the development phase of a speech enhancer, intrusive intelligibility metrics can provide an alternative to listening tests. This thesis begins by outlining a new intrusive intelligibility metric, WSTOI, that is a development of the existing STOI metric. WSTOI improves STOI by weighting the intelligibility contributions of different time-frequency regions with an estimate of their intelligibility content. The prediction accuracies of WSTOI and STOI are compared for a range of noises and noise suppression algorithms and it is found that WSTOI outperforms STOI in all tested conditions. The thesis then investigates the best choice of mask-estimation algorithm, target mask, and method of applying the estimated mask. A new target mask, the HSWOBM, is proposed that optimises a modified version of WSTOI with a higher frequency resolution. The HSWOBM is optimised for a stochastic noise signal to encourage a mask estimator trained on the HSWOBM to generalise better to unseen noise conditions. A high frequency resolution version of WSTOI is optimised as this gives improvements in predicted quality compared with optimising WSTOI. Of the tested approaches to target mask estimation, the best-performing approach uses a feed-forward neural network with a loss function based on WSTOI. The best-performing feature set is based on the gains produced by a classical speech enhancer and an estimate of the local voiced-speech-plus-noise to noise ratio in different time-frequency regions, which is obtained with the aid of a pitch estimator. When the estimated target mask is applied in the conventional way, by multiplying the speech by the mask in the time-frequency domain, it can result in speech with very poor perceptual quality. The final chapter of this thesis therefore investigates alternative approaches to applying the estimated mask to the noisy speech, in order to improve both intelligibility and quality. An approach is developed that uses the mask to supply prior information about the speech presence probability to a classical speech enhancer that minimises the expected squared error in the log spectral amplitudes. The proposed end-to-end enhancer outperforms existing algorithms in terms of predicted quality and intelligibility for most noise types.Open Acces

Deep Residual-Dense Lattice Network for Speech Enhancement

Convolutional neural networks (CNNs) with residual links (ResNets) and causal dilated convolutional units have been the network of choice for deep learning approaches to speech enhancement. While residual links improve gradient flow during training, feature diminution of shallow layer outputs can occur due to repetitive summations with deeper layer outputs. One strategy to improve feature re-usage is to fuse both ResNets and densely connected CNNs (DenseNets). DenseNets, however, over-allocate parameters for feature re-usage. Motivated by this, we propose the residual-dense lattice network (RDL-Net), which is a new CNN for speech enhancement that employs both residual and dense aggregations without over-allocating parameters for feature re-usage. This is managed through the topology of the RDL blocks, which limit the number of outputs used for dense aggregations. Our extensive experimental investigation shows that RDL-Nets are able to achieve a higher speech enhancement performance than CNNs that employ residual and/or dense aggregations. RDL-Nets also use substantially fewer parameters and have a lower computational requirement. Furthermore, we demonstrate that RDL-Nets outperform many state-of-the-art deep learning approaches to speech enhancement.Comment: 8 pages, Accepted by AAAI-202

Data-driven Speech Intelligibility Enhancement and Prediction for Hearing Aids

Hearing impairment is a widespread problem around the world. It is estimated that one in six people are living with some degree of hearing loss. Moderate and severe hearing impairment has been recognised as one of the major causes of disability, which is associated with declines in the quality of life, mental illness and dementia. However, investigation shows that only 10-20\% of older people with significant hearing impairment wear hearing aids. One of the main factors causing the low uptake is that current devices struggle to help hearing aid users understand speech in noisy environments. For the purpose of compensating for the elevated hearing thresholds and dysfunction of source separation processing caused by the impaired auditory system, amplification and denoising have been the major focuses of current hearing aid studies to improve the intelligibility of speech in noise. Also, it is important to derive a metric that can fairly predict speech intelligibility for the better development of hearing aid techniques. This thesis aims to enhance the speech intelligibility of hearing impaired listeners. Motivated by the success of data-driven approaches in many speech processing applications, this work proposes the differentiable hearing aid speech processing (DHASP) framework to optimise both the amplification and denoising modules within a hearing aid processor. This is accomplished by setting an intelligibility-based optimisation objective and taking advantage of large-scale speech databases to train the hearing aid processor to maximise the intelligibility for the listeners. The first set of experiments is conducted on both clean and noisy speech databases, and the results from objective evaluation suggest that the amplification fittings optimised within the DHASP framework can outperform a widely used and well-recognised fitting. The second set of experiments is conducted on a large-scale database with simulated domestic noisy scenes. The results from both objective and subjective evaluations show that the DHASP-optimised hearing aid processor incorporating a deep neural network-based denoising module can achieve competitive performance in terms of intelligibility enhancement. A precise intelligibility predictor can provide reliable evaluation results to save the cost of expensive and time-consuming subjective evaluation. Inspired by the findings that automatic speech recognition (ASR) models show similar recognition results as humans in some experiments, this work exploits ASR models for intelligibility prediction. An intrusive approach using ASR hidden representations and a non-intrusive approach using ASR uncertainty are proposed and explained in the third and fourth experimental chapters. Experiments are conducted on two databases, one with monaural speech in speech-spectrum-shaped noise with normal hearing listeners, and the other one with processed binaural speech in domestic noise with hearing impaired listeners. Results suggest that both the intrusive and non-intrusive approaches can achieve top performances and outperform a number of widely used intelligibility prediction approaches. In conclusion, this thesis covers both the enhancement and prediction of speech intelligibility for hearing aids. The proposed hearing aid processor optimised within the proposed DHASP framework can significantly improve the intelligibility of speech in noise for hearing impaired listeners. Also, it is shown that the proposed ASR-based intelligibility prediction approaches can achieve state-of-the-art performances against a number of widely used intelligibility predictors