Automatic speech recognition with deep neural networks for impaired speech

The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13% for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.Peer ReviewedPostprint (author's final draft

Deep neural networks in acoustic model

L'estudiant m'ha contactat amb el requeriment d'una oferta per matricular-se i aquesta oferta respon a la seva petició. Després de confirmar amb Secretaria Acadèmica que està acceptat a destinació, deixem títol, descripció, objectius, i tutor extern per determinar quan arribi a destí.Do implementation of a training of a deep neural network acoustic model for speech recognitio

An analysis of the short utterance problem for speaker characterization

Speaker characterization has always been conditioned by the length of the evaluated utterances. Despite performing well with large amounts of audio, significant degradations in performance are obtained when short utterances are considered. In this work we present an analysis of the short utterance problem providing an alternative point of view. From our perspective the performance in the evaluation of short utterances is highly influenced by the phonetic similarity between enrollment and test utterances. Both enrollment and test should contain similar phonemes to properly discriminate, being degraded otherwise. In this study we also interpret short utterances as incomplete long utterances where some acoustic units are either unbalanced or just missing. These missing units are responsible for the speaker representations to be unreliable. These unreliable representations are biased with respect to the reference counterparts, obtained from long utterances. These undesired shifts increase the intra-speaker variability, causing a significant loss of performance. According to our experiments, short utterances (3-60 s) can perform as accurate as if long utterances were involved by just reassuring the phonetic distributions. This analysis is determined by the current embedding extraction approach, based on the accumulation of local short-time information. Thus it is applicable to most of the state-of-the-art embeddings, including traditional i-vectors and Deep Neural Network (DNN) xvectors

Low latency modeling of temporal contexts for speech recognition

This thesis focuses on the development of neural network acoustic models for large vocabulary continuous speech recognition (LVCSR) to satisfy the design goals of low latency and low computational complexity. Low latency enables online speech recognition; and low computational complexity helps reduce the computational cost both during training and inference. Long span sequential dependencies and sequential distortions in the input vector sequence are a major challenge in acoustic modeling. Recurrent neural networks have been shown to effectively model these dependencies. Specifically, bidirectional long short term memory (BLSTM) networks, provide state-of-the-art performance across several LVCSR tasks. However the deployment of bidirectional models for online LVCSR is non-trivial due to their large latency; and unidirectional LSTM models are typically preferred. In this thesis we explore the use of hierarchical temporal convolution to model long span temporal dependencies. We propose a sub-sampled variant of these temporal convolution neural networks, termed time-delay neural networks (TDNNs). These sub-sampled TDNNs reduce the computation complexity by ~5x, compared to TDNNs, during frame randomized pre-training. These models are shown to be effective in modeling long-span temporal contexts, however there is a performance gap compared to (B)LSTMs. As recent advancements in acoustic model training have eliminated the need for frame randomized pre-training we modify the TDNN architecture to use higher sampling rates, as the increased computation can be amortized over the sequence. These variants of sub- sampled TDNNs provide performance superior to unidirectional LSTM networks, while also affording a lower real time factor (RTF) during inference. However we show that the BLSTM models outperform both the TDNN and LSTM models. We propose a hybrid architecture interleaving temporal convolution and LSTM layers which is shown to outperform the BLSTM models. Further we improve these BLSTM models by using higher frame rates at lower layers and show that the proposed TDNN- LSTM model performs similar to these superior BLSTM models, while reducing the overall latency to 200 ms. Finally we describe an online system for reverberation robust ASR, using the above described models in conjunction with other data augmentation techniques like reverberation simulation, which simulates far-field environments, and volume perturbation, which helps tackle volume variation even without gain normalization