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

Using audio and visual information for single channel speaker separation

This work proposes a method to exploit both audio and vi- sual speech information to extract a target speaker from a mix- ture of competing speakers. The work begins by taking an ef- fective audio-only method of speaker separation, namely the soft mask method, and modifying its operation to allow visual speech information to improve the separation process. The au- dio input is taken from a single channel and includes the mix- ture of speakers, where as a separate set of visual features are extracted from each speaker. This allows modification of the separation process to include not only the audio speech but also visual speech from each speaker in the mixture. Experimen- tal results are presented that compare the proposed audio-visual speaker separation with audio-only and visual-only methods us- ing both speech quality and speech intelligibility metrics

Reconstructing intelligible audio speech from visual speech features

This work describes an investigation into the feasibility of producing intelligible audio speech from only visual speech fea- tures. The proposed method aims to estimate a spectral enve- lope from visual features which is then combined with an arti- ficial excitation signal and used within a model of speech pro- duction to reconstruct an audio signal. Different combinations of audio and visual features are considered, along with both a statistical method of estimation and a deep neural network. The intelligibility of the reconstructed audio speech is measured by human listeners, and then compared to the intelligibility of the video signal only and when combined with the reconstructed audio

Voicing classification of visual speech using convolutional neural networks

The application of neural network and convolutional neural net- work (CNN) architectures is explored for the tasks of voicing classification (classifying frames as being either non-speech, unvoiced, or voiced) and voice activity detection (VAD) of vi- sual speech. Experiments are conducted for both speaker de- pendent and speaker independent scenarios. A Gaussian mixture model (GMM) baseline system is de- veloped using standard image-based two-dimensional discrete cosine transform (2D-DCT) visual speech features, achieving speaker dependent accuracies of 79% and 94%, for voicing classification and VAD respectively. Additionally, a single- layer neural network system trained using the same visual fea- tures achieves accuracies of 86 % and 97 %. A novel technique using convolutional neural networks for visual speech feature extraction and classification is presented. The voicing classifi- cation and VAD results using the system are further improved to 88 % and 98 % respectively. The speaker independent results show the neural network system to outperform both the GMM and CNN systems, achiev- ing accuracies of 63 % for voicing classification, and 79 % for voice activity detection

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

HMM-Based Speech Enhancement Using Sub-Word Models and Noise Adaptation

This work proposes a method of speech enhancement that uses a network of HMMs to first decode noisy speech and to then synthesise a set of features that enables a clean speech signal to be reconstructed. Different choices of acoustic model (whole-word, monophone and triphone) and grammars (highly constrained to no constraints) are considered and the effects of introducing or relaxing acoustic and grammar constraints investigated. For robust operation in noisy conditions it is necessary for the HMMs to model noisy speech and consequently noise adaptation is investigated along with its effect on the reconstructed speech. Speech quality and intelligibility analysis find triphone models with no grammar, combined with noise adaptation, gives highest performance that outperforms conventional methods of enhancement at low signal-to-noise ratios

Visual speech synthesis using dynamic visemes, contextual features and DNNs

This paper examines methods to improve visual speech synthesis from a text input using a deep neural network (DNN). Two representations of the input text are considered, namely into phoneme sequences or dynamic viseme sequences. From these sequences, contextual features are extracted that include information at varying linguistic levels, from frame level down to the utterance level. These are extracted from a broad sliding window that captures context and produces features that are input into the DNN to estimate visual features. Experiments first compare the accuracy of these visual features against an HMM baseline method which establishes that both the phoneme and dynamic viseme systems perform better with best performance obtained by a combined phoneme-dynamic viseme system. An investigation into the features then reveals the importance of the frame level information which is able to avoid discontinuities in the visual feature sequence and produces a smooth and realistic output

Generating intelligible audio speech from visual speech

This work is concerned with generating intelligible audio speech from a video of a person talking. Regression and classification methods are proposed first to estimate static spectral envelope features from active appearance model (AAM) visual features. Two further methods are then developed to incorporate temporal information into the prediction - a feature-level method using multiple frames and a model-level method based on recurrent neural networks. Speech excitation information is not available from the visual signal, so methods to artificially generate aperiodicity and fundamental frequency are developed. These are combined within the STRAIGHT vocoder to produce a speech signal. The various systems are optimised through objective tests before applying subjective intelligibility tests that determine a word accuracy of 85% from a set of human listeners on the GRID audio-visual speech database. This compares favourably with a previous regression-based system that serves as a baseline which achieved a word accuracy of 33%