Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Prosody generation for text-to-speech synthesis

The absence of convincing intonation makes current parametric speech synthesis systems sound dull and lifeless, even when trained on expressive speech data. Typically, these systems use regression techniques to predict the fundamental frequency (F0) frame-by-frame. This approach leads to overlysmooth pitch contours and fails to construct an appropriate prosodic structure across the full utterance. In order to capture and reproduce larger-scale pitch patterns, we propose a template-based approach for automatic F0 generation, where per-syllable pitch-contour templates (from a small, automatically learned set) are predicted by a recurrent neural network (RNN). The use of syllable templates mitigates the over-smoothing problem and is able to reproduce pitch patterns observed in the data. The use of an RNN, paired with connectionist temporal classification (CTC), enables the prediction of structure in the pitch contour spanning the entire utterance. This novel F0 prediction system is used alongside separate LSTMs for predicting phone durations and the other acoustic features, to construct a complete text-to-speech system. Later, we investigate the benefits of including long-range dependencies in duration prediction at frame-level using uni-directional recurrent neural networks. Since prosody is a supra-segmental property, we consider an alternate approach to intonation generation which exploits long-term dependencies of F0 by effective modelling of linguistic features using recurrent neural networks. For this purpose, we propose a hierarchical encoder-decoder and multi-resolution parallel encoder where the encoder takes word and higher level linguistic features at the input and upsamples them to phone-level through a series of hidden layers and is integrated into a Hybrid system which is then submitted to Blizzard challenge workshop. We then highlight some of the issues in current approaches and a plan for future directions of investigation is outlined along with on-going work

Discriminative features for GMM and i-vector based speaker diarization

Speaker diarization has received several research attentions over the last decade. Among the different domains of speaker diarization, diarization in meeting domain is the most challenging one. It usually contains spontaneous speech and is, for example, susceptible to reverberation. The appropriate selection of speech features is one of the factors that affect the performance of speaker diarization systems. Mel Frequency Cepstral Coefficients (MFCC) are the most widely used short-term speech features in speaker diarization. Other factors that affect the performance of speaker diarization systems are the techniques employed to perform both speaker segmentation and speaker clustering. In this thesis, we have proposed the use of jitter and shimmer long-term voice-quality features both for Gaussian Mixture Modeling (GMM) and i-vector based speaker diarization systems. The voice-quality features are used together with the state-of-the-art short-term cepstral and long-term speech ones. The long-term features consist of prosody and Glottal-to-Noise excitation ratio (GNE) descriptors. Firstly, the voice-quality, prosodic and GNE features are stacked in the same feature vector. Then, they are fused with cepstral coefficients at the score likelihood level both for the proposed Gaussian Mixture Modeling (GMM) and i-vector based speaker diarization systems. For the proposed GMM based speaker diarization system, independent HMM models are estimated from the short-term and long-term speech feature sets. The fusion of the short-term descriptors with the long-term ones in speaker segmentation is carried out by linearly weighting the log-likelihood scores of Viterbi decoding. In the case of speaker clustering, the fusion of the short-term cepstral features with the long-term ones is carried out by linearly fusing the Bayesian Information Criterion (BIC) scores corresponding to these feature sets. For the proposed i-vector based speaker diarization system, the speaker segmentation is carried out exactly the same as in the previously mentioned GMM based speaker diarization system. However, the speaker clustering technique is based on the recently introduced factor analysis paradigm. Two set of i-vectors are extracted from the speaker segmentation hypothesis. Whilst the first i-vector is extracted from short-term cepstral features, the second one is extracted from the voice quality, prosody and GNE descriptors. Then, the cosine-distance and Probabilistic Linear Discriminant Analysis (PLDA) scores of i-vectors are linearly weighted to obtain a fused similarity score. Finally, the fused score is used as speaker clustering distance. We have also proposed the use of delta dynamic features for speaker clustering. The motivation for using deltas in clustering is that delta dynamic features capture the transitional characteristics of the speech signal which contain speaker specific information. This information is not captured by the static cepstral coefficients. The delta features are used together with the short-term static cepstral coefficients and long-term speech features (i.e., voice-quality, prosody and GNE) both for GMM and i-vector based speaker diarization systems. The experiments have been carried out on Augmented Multi-party Interaction (AMI) meeting corpus. The experimental results show that the use of voice-quality, prosody, GNE and delta dynamic features improve the performance of both GMM and i-vector based speaker diarization systems.La diarización del altavoz ha recibido varias atenciones de investigación durante la última década. Entre los diferentes dominios de la diarización del hablante, la diarización en el dominio del encuentro es la más difícil. Normalmente contiene habla espontánea y, por ejemplo, es susceptible de reverberación. La selección apropiada de las características del habla es uno de los factores que afectan el rendimiento de los sistemas de diarización de los altavoces. Los Coeficientes Cepstral de Frecuencia Mel (MFCC) son las características de habla de corto plazo más utilizadas en la diarización de los altavoces. Otros factores que afectan el rendimiento de los sistemas de diarización del altavoz son las técnicas empleadas para realizar tanto la segmentación del altavoz como el agrupamiento de altavoces. En esta tesis, hemos propuesto el uso de jitter y shimmer características de calidad de voz a largo plazo tanto para GMM y i-vector basada en sistemas de diarización de altavoces. Las características de calidad de voz se utilizan junto con el estado de la técnica a corto plazo cepstral y de larga duración de habla. Las características a largo plazo consisten en la prosodia y los descriptores de relación de excitación Glottal-a-Ruido (GNE). En primer lugar, las características de calidad de voz, prosódica y GNE se apilan en el mismo vector de características. A continuación, se fusionan con coeficientes cepstrales en el nivel de verosimilitud de puntajes tanto para los sistemas de diarización de altavoces basados ¿¿en el modelo Gaussian Mixture Modeling (GMM) como en los sistemas basados ¿¿en i-vector. . Para el sistema de diarización de altavoces basado en GMM propuesto, se calculan modelos HMM independientes a partir de cada conjunto de características. En la segmentación de los altavoces, la fusión de los descriptores a corto plazo con los de largo plazo se lleva a cabo mediante la ponderación lineal de las puntuaciones log-probabilidad de decodificación Viterbi. En la agrupación de altavoces, la fusión de las características cepstrales a corto plazo con las de largo plazo se lleva a cabo mediante la fusión lineal de las puntuaciones Bayesian Information Criterion (BIC) correspondientes a estos conjuntos de características. Para el sistema de diarización de altavoces basado en un vector i, la fusión de características se realiza exactamente igual a la del sistema basado en GMM antes mencionado. Sin embargo, la técnica de agrupación de altavoces se basa en el paradigma de análisis de factores recientemente introducido. Dos conjuntos de i-vectores se extraen de la hipótesis de segmentación de altavoz. Mientras que el primer vector i se extrae de características espectrales a corto plazo, el segundo se extrae de los descriptores de calidad de voz apilados, prosódicos y GNE. A continuación, las puntuaciones de coseno-distancia y Probabilistic Linear Discriminant Analysis (PLDA) entre i-vectores se ponderan linealmente para obtener una puntuación de similitud fundida. Finalmente, la puntuación fusionada se utiliza como distancia de agrupación de altavoces. También hemos propuesto el uso de características dinámicas delta para la agrupación de locutores. La motivación para el uso de deltas en la agrupación es que las características dinámicas delta capturan las características de transición de la señal de voz que contienen información específica del locutor. Esta información no es capturada por los coeficientes cepstrales estáticos. Las características delta se usan junto con los coeficientes cepstrales estáticos a corto plazo y las características de voz a largo plazo (es decir, calidad de voz, prosodia y GNE) tanto para sistemas de diarización de altavoces basados en GMM como en sistemas i-vector. Los resultados experimentales sobre AMI muestran que el uso de calidad vocal, prosódica, GNE y dinámicas delta mejoran el rendimiento de los sistemas de diarización de altavoces basados en GMM e i-vector.Postprint (published version

Controllable music performance synthesis via hierarchical modelling

L’expression musicale requiert le contrôle sur quelles notes sont jouées ainsi que comment elles se jouent. Les synthétiseurs audios conventionnels offrent des contrôles expressifs détaillés, cependant au détriment du réalisme. La synthèse neuronale en boîte noire des audios et les échantillonneurs concaténatifs sont capables de produire un son réaliste, pourtant, nous avons peu de mécanismes de contrôle. Dans ce travail, nous introduisons MIDI-DDSP, un modèle hiérarchique des instruments musicaux qui permet tant la synthèse neuronale réaliste des audios que le contrôle sophistiqué de la part des utilisateurs. À partir des paramètres interprétables de synthèse provenant du traitement différentiable des signaux numériques (Differentiable Digital Signal Processing, DDSP), nous inférons les notes musicales et la propriété de haut niveau de leur performance expressive (telles que le timbre, le vibrato, l’intensité et l’articulation). Ceci donne naissance à une hiérarchie de trois niveaux (notes, performance, synthèse) qui laisse aux individus la possibilité d’intervenir à chaque niveau, ou d’utiliser la distribution préalable entraînée (notes étant donné performance, synthèse étant donné performance) pour une assistance créative. À l’aide des expériences quantitatives et des tests d’écoute, nous démontrons que cette hiérarchie permet de reconstruire des audios de haute fidélité, de prédire avec précision les attributs de performance d’une séquence de notes, mais aussi de manipuler indépendamment les attributs étant donné la performance. Comme il s’agit d’un système complet, la hiérarchie peut aussi générer des audios réalistes à partir d’une nouvelle séquence de notes. En utilisant une hiérarchie interprétable avec de multiples niveaux de granularité, MIDI-DDSP ouvre la porte aux outils auxiliaires qui renforce la capacité des individus à travers une grande variété d’expérience musicale.Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience

Deep Learning for Automatic Assessment and Feedback of Spoken English

Growing global demand for learning a second language (L2), particularly English, has led to considerable interest in automatic spoken language assessment, whether for use in computerassisted language learning (CALL) tools or for grading candidates for formal qualifications. This thesis presents research conducted into the automatic assessment of spontaneous nonnative English speech, with a view to be able to provide meaningful feedback to learners. One of the challenges in automatic spoken language assessment is giving candidates feedback on particular aspects, or views, of their spoken language proficiency, in addition to the overall holistic score normally provided. Another is detecting pronunciation and other types of errors at the word or utterance level and feeding them back to the learner in a useful way. It is usually difficult to obtain accurate training data with separate scores for different views and, as examiners are often trained to give holistic grades, single-view scores can suffer issues of consistency. Conversely, holistic scores are available for various standard assessment tasks such as Linguaskill. An investigation is thus conducted into whether assessment scores linked to particular views of the speaker’s ability can be obtained from systems trained using only holistic scores. End-to-end neural systems are designed with structures and forms of input tuned to single views, specifically each of pronunciation, rhythm, intonation and text. By training each system on large quantities of candidate data, individual-view information should be possible to extract. The relationships between the predictions of each system are evaluated to examine whether they are, in fact, extracting different information about the speaker. Three methods of combining the systems to predict holistic score are investigated, namely averaging their predictions and concatenating and attending over their intermediate representations. The combined graders are compared to each other and to baseline approaches. The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. An approach to these tasks is presented by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora x of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level

SYNTHESIZING DYSARTHRIC SPEECH USING MULTI-SPEAKER TTS FOR DSYARTHRIC SPEECH RECOGNITION

Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems may help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech is required, which is not readily available for dysarthric talkers. In this dissertation, we investigate dysarthric speech augmentation and synthesis methods. To better understand differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels, a comparative study between typical and dysarthric speech was conducted. These characteristics are important components for dysarthric speech modeling, synthesis, and augmentation. For augmentation, prosodic transformation and time-feature masking have been proposed. For dysarthric speech synthesis, this dissertation has introduced a modified neural multi-talker TTS by adding a dysarthria severity level coefficient and a pause insertion model to synthesize dysarthric speech for varying severity levels. In addition, we have extended this work by using a label propagation technique to create more meaningful control variables such as a continuous Respiration, Laryngeal and Tongue (RLT) parameter, even for datasets that only provide discrete dysarthria severity level information. This approach increases the controllability of the system, so we are able to generate more dysarthric speech with a broader range. To evaluate their effectiveness for synthesis of training data, dysarthria-specific speech recognition was used. Results show that a DNN-HMM model trained on additional synthetic dysarthric speech achieves WER improvement of 12.2% compared to the baseline, and that the addition of the severity level and pause insertion controls decrease WER by 6.5%, showing the effectiveness of adding these parameters. Overall results on the TORGO database demonstrate that using dysarthric synthetic speech to increase the amount of dysarthric-patterned speech for training has a significant impact on the dysarthric ASR systems