Data-driven deep modeling and training for automatic speech recognition

Many of today’s state-of-the-art automatic speech recognition (ASR) systems are based on hybrid hidden Markov models (HMM) that rely on neural networks to provide acoustic and language model probabilities. A dynamic programming decoder then uses these probabilities to find and output the most likely sequence of words for a given input audio signal. As one of the prerequisites, the acoustic model needs to be trained on a collection of transcribed recordings: this acoustic training is the main focus of this thesis. Commonly, the input signal is represented as a sequence of relatively low-dimensional feature vectors that capture the distribution of short-time energy within a sliding analysis window. The process of conventional feature extraction is based on time-frequency decomposition of the onedimensional input signal using either Fourier transform or a bank of band pass filters. The first part of this thesis centers around the question: to which extent can the feature extraction be learned by the acoustic model from transcribed audio data, without manually specifying a pre-processing pipeline? We will show that not only can a neural network learn to classify the HMM states from the raw time signal, but also learn to perform the time-frequency decomposition in its input layer. Inspired by this finding, we will replace the fully-connected input layer by a convolutional layer that is well suited to assist the neural network in learning shift-invariant patterns, and whose operation can be interpreted as filtering in time. Such models show very similar error rates, while not yet outperforming the hand-crafted feature extraction pipeline. In the second part, we will investigate the objective function that is optimized during the supervised acoustic training. While it is commonly believed that softmax normalization in the output layer requires the training to minimize the negative log posterior probability of the data (cross entropy), there is no reason to assume that it cannot be done with other objective functions. In fact, both cross entropy and squared error can be shown to be upper bound of the difference between the Bayes error and the model error (i.e. the difference that arises when the decision rule relies on a model instead of the true probability). We will demonstrate how a hybrid acoustic model can be trained using squared error criterion, and its accuracy improved by initializing it with a model pre-trained with the cross entropy criterion. In the third part of this study, we will investigate how i-vectors can be used for acoustic adaptation, and show that they can help obtain a consistent reduction of word error rate on multiple tasks. Additionally, we will outline a careful analysis of different integration strategies as well as of the parameters of the i-vector extraction pipeline. Finally, in the fourth part of this thesis we will apply these and other methods to the task of speech recognition and keyword search on low-resource languages. The limited amount of available resources makes the acoustic training extremely challenging. We will present a series of experiments performed in the scope of the IARPA Babel project that make heavy use of multilingual bottleneck features and explore techniques such as semi-supervised training, graphemic pronunciation modeling, and handling of keywords not observed during training.