You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation

Data augmentation is one of the most effective ways to make end-to-end automatic speech recognition (ASR) perform close to the conventional hybrid approach, especially when dealing with low-resource tasks. Using recent advances in speech synthesis (text-to-speech, or TTS), we build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. We argue that, when the training data amount is relatively low, this approach can allow an end-to-end model to reach hybrid systems' quality. For an artificial low-to-medium-resource setup, we compare the proposed augmentation with the semi-supervised learning technique. We also investigate the influence of vocoder usage on final ASR performance by comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an external language model, our approach outperforms a semi-supervised setup for LibriSpeech test-clean and only 33% worse than a comparable supervised setup. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other

Difference target propagation

Backpropagation has been the workhorse of recent successes of deep learning but it relies on infinitesimal effects (partial derivatives) in order to perform credit assignment. This could become a serious issue as one considers deeper and more non-linear functions, e.g., consider the extreme case of non-linearity where the relation between parameters and cost is actually discrete. Inspired by the biological implausibility of Backpropagation, this thesis proposes a novel approach, Target Propagation. The main idea is to compute targets rather than gradients, at each layer in which feedforward and feedback networks form Auto-Encoders. We show that a linear correction for the imperfectness of the Auto-Encoders, called Difference Target Propagation is very effective to make Target Propagation actually work, leading to results comparable to Backpropagation for deep networks with discrete and continuous units, Denoising Auto-Encoders and achieving state of the art for stochastic networks. In Chapters 1, we introduce several classical learning rules in Deep Neural Networks, including Backpropagation and more biological plausible learning rules. In Chapters 2 and 3, we introduce a novel approach, Target Propagation, more biological plausible learning rule than Backpropagation. In addition, we show that Target Propagation is comparable to Backpropagation in Deep Neural Networks.L'algorithme de r etropropagation a et e le cheval de bataille du succ es r ecent de l'apprentissage profond, mais elle s'appuie sur des e ets in nit esimaux (d eriv ees partielles) a n d'e ectuer l'attribution de cr edit. Cela pourrait devenir un probl eme s erieux si l'on consid ere des fonctions plus profondes et plus non lin eaires, avec a l'extr^eme la non-lin earit e o u la relation entre les param etres et le co^ut est r eellement discr ete. Inspir ee par la pr esum ee invraisemblance biologique de la r etropropagation, cette th ese propose une nouvelle approche, Target Propagation. L'id ee principale est de calculer des cibles plut^ot que des gradients a chaque couche, en faisant en sorte que chaque paire de couches successive forme un auto-encodeur. Nous montrons qu'une correction lin eaire, appel ee Di erence Target Propaga- tion, est tr es e cace, conduisant a des r esultats comparables a la r etropropagation pour les r eseaux profonds avec des unit es discr etes et continues et des auto- encodeurs et atteignant l' etat de l'art pour les r eseaux stochastiques