Analysing the importance of different visual feature coefficients

A study is presented to determine the relative importance of different visual features for speech recognition which includes pixel-based, model-based, contour-based and physical features. Analysis to determine the discriminability of features is per- formed through F-ratio and J-measures for both static and tem- poral derivatives, the results of which were found to correlate highly with speech recognition accuracy (r = 0.97). Princi- pal component analysis is then used to combine all visual fea- tures into a single feature vector, of which further analysis is performed on the resulting basis functions. An optimal feature vector is obtained which outperforms the best individual feature (AAM) with 93.5 % word accuracy

Objective measures for predicting the intelligibility of spectrally smoothed speech with artificial excitation

A study is presented on how well objective measures of speech quality and intelligibility can predict the subjective in- telligibility of speech that has undergone spectral envelope smoothing and simplification of its excitation. Speech modi- fications are made by resynthesising speech that has been spec- trally smoothed. Objective measures are applied to the mod- ified speech and include measures of speech quality, signal- to-noise ratio and intelligibility, as well as proposing the nor- malised frequency-weighted spectral distortion (NFD) measure. The measures are compared to subjective intelligibility scores where it is found that several have high correlation (|r| ≥ 0.7), with NFD achieving the highest correlation (r = −0.81

A Comparison of Perceptually Motivated Loss Functions for Binary Mask Estimation in Speech Separation

This work proposes and compares perceptually motivated loss functions for deep learning based binary mask estimation for speech separation. Previous loss functions have focused on maximising classification accuracy of mask estimation but we now propose loss functions that aim to maximise the hit mi- nus false-alarm (HIT-FA) rate which is known to correlate more closely to speech intelligibility. The baseline loss function is bi- nary cross-entropy (CE), a standard loss function used in binary mask estimation, which maximises classification accuracy. We propose first a loss function that maximises the HIT-FA rate in- stead of classification accuracy. We then propose a second loss function that is a hybrid between CE and HIT-FA, providing a balance between classification accuracy and HIT-FA rate. Eval- uations of the perceptually motivated loss functions with the GRID database show improvements to HIT-FA rate and ESTOI across babble and factory noises. Further tests then explore ap- plication of the perceptually motivated loss functions to a larger vocabulary dataset

Audio speech enhancement using masks derived from visual speech

The aim of the work in this thesis is to explore how visual speech can be used within monaural masking based speech enhancement to remove interfering noise, with a focus on improving intelligibility. Visual speech has the advantage of not being corrupted by interfering noise and can therefore provide additional information within a speech enhancement framework. More specifically, this work considers audio-only, visual-only and audio-visual methods of mask estimation within deep learning architectures with application to both seen and unseen noise types. To estimate masks from audio and visual speech information, models are developed using deep neural networks, specifically feed-forward (DNN) and recurrent (RNN) neural networks for temporal modelling and convolutional neural networks (CNN) for visual feature extraction. It was found that the proposed layer normalised bi-directional feed-forward hybrid network using gated recurrent units (LNBiGRUDNN) provided best performance across all objective measures for temporal modelling. Also, extracting visual features using both pre-trained and end-to-end trained CNNs outperform traditional active appearance model (AAM) feature extraction across all noise types and SNRs tested. End-to-end CNNs trained on images focused on mouth-only regions-of-interest provided best performance for both audio-visual and visual-only models. The best performing audio-visual masking method outperformed both audio-only and visual-only masking methods in both matched and unseen noise type and SNR dependent conditions. For example, in unseen cafeteria babble noise at -10 dB, audio-visual masking had an ESTOI of 46.8, while audio-only and visual-only masking scored 15.0 and 42.4, and the unprocessed audio scored 9.3. Formal tests show that visual information is critical for improving intelligibility at low SNRs and for generalisation to unseen noise conditions. Experiments in large unconstrained vocabulary speech confirm that the model architectures and approaches developed can generalise to unconstrained speech across noise independent conditions and can be considered for monaural speaker dependent real-world applications

The Effect of Real-Time Constraints on Automatic Speech Animation

Machine learning has previously been applied successfully to speech-driven facial animation. To account for carry-over and anticipatory coarticulation a common approach is to predict the facial pose using a symmetric window of acoustic speech that includes both past and future context. Using future context limits this approach for animating the faces of characters in real-time and networked applications, such as online gaming. An acceptable latency for conversational speech is 200ms and typically network transmission times will consume a significant part of this. Consequently, we consider asymmetric windows by investigating the extent to which decreasing the future context effects the quality of predicted animation using both deep neural networks (DNNs) and bi-directional LSTM recurrent neural networks (BiLSTMs). Specifically we investigate future contexts from 170ms (fully-symmetric) to 0ms (fullyasymmetric

Speaker-independent speech animation using perceptual loss functions and synthetic data

We propose a real-time speaker-independent speech- to-facial animation system that predicts lip and jaw movements on a reference face for audio speech taken from any speaker. Our approach is motivated by two key observations; 1) Speaker- independent facial animation can be generated from phoneme labels, but to perform this automatically a speech recogniser is needed which, due to contextual look-ahead, introduces too much time lag. 2) Audio-driven speech animation can be performed in real-time but requires large, multi-speaker audio-visual speech datasets of which there are few. We adopt a novel three- stage training procedure that leverages the advantages of each approach. First we train a phoneme-to-visual speech model from a large single-speaker audio-visual dataset. Next, we use this model to generate the synthetic visual component of a large multi-speaker audio dataset of which the video is not available. Finally, we learn an audio-to-visual speech mapping using the synthetic visual features as the target. Furthermore, we increase the realism of the predicted facial animation by introducing two perceptually-based loss functions that aim to improve mouth closures and openings. The proposed method and loss functions are evaluated objectively using mean square error, global variance and a new metric that measures the extent of mouth opening. Subjective tests show that our approach produces facial animation comparable to those produced from phoneme sequences and that improved mouth closures, particularly for bilabial closures, are achieved

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination- and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists’ scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications

Using visual speech information and perceptually motivated loss functions for binary mask estimation

This work is concerned with using deep neural networks for estimating binary masks within a speech enhancement framework. We first examine the effect of supplementing the audio features used in mask estimation with visual speech information. Visual speech is known to be robust to noise although not necessarily as discriminative as audio features, particularly at higher signal-to-noise ratios. Furthermore, most DNN approaches to mask estimate use the cross-entropy (CE) loss function which aims to maximise classification accuracy. However, we first propose a loss function that aims to maximise the hit minus false-alarm (HIT-FA) rate of the mask, which is known to correlate more closely to speech intelligibility than classification accuracy. We then extend this to a hybrid loss function that combines both the CE and HIT-FA loss functions to provide a balance between classification accuracy and HIT-FA rate of the resulting masks. Evaluations of the perceptually motivated loss functions are carried out using the GRID and larger RM-3000 datasets and show improvements to HIT-FA rate and ESTOI across all noises and SNRs tested. Tests also found that supplementing audio with visual information into a single bimodal audio-visual system gave best performance for all measures and conditions tested