Joint Localization and Classification of Multiple Sound Sources Using a Multi-task Neural Network

We propose a novel multi-task neural network-based approach for joint sound source localization and speech/non-speech classification in noisy environments. The network takes raw short time Fourier transform as input and outputs the likelihood values for the two tasks, which are used for the simultaneous detection, localization and classification of an unknown number of overlapping sound sources, Tested with real recorded data, our method achieves significantly better performance in terms of speech/non-speech classification and localization of speech sources, compared to method that performs localization and classification separately. In addition, we demonstrate that incorporating the temporal context can further improve the performance.

Novel Methods for Incorporating Prior Knowledge for Automatic Speech Assessment

Subrahmanya Pavankumar Dubagunta

Speech signal conveys several kinds of information such as a message, speaker identity, emotional state of the speaker and social state of the speaker. Automatic speech assessment is a broad area that refers to using automatic methods to predict human judgements regarding different kinds of information conveyed in speech, such as intelligibility of the spoken message, dialect and fluency of the speaker. Unlike other speech technology areas, such as automatic speech recognition, text-to-speech synthesis and automatic speaker recognition, automatic speech assessment is an emerging direction of research. One of the challenges in this field is that there is no single method or framework that scales across diverse speech assessment tasks. Thus, this thesis takes a broader outlook and focuses on prior knowledge incorporation for diverse data-driven speech assessment problems.First, we focus on the development of end-to-end acoustic modelling methods for non-verbal cue-based speech assessment. More precisely, we develop neural network-based methods that can integrate prior knowledge about speech production to learn to assess speech from raw waveform. We validate the developed methods through investigations on several speech assessment tasks, viz. dialect identification, depression detection and speech fluency rating prediction.Second, we focus on advancing a recently proposed phone posterior feature-based intelligibility estimation technique. Specifically, to enhance phone posterior probability estimation, we propose two novel approaches to incorporate linguistic segment level knowledge during the training of neural networks through estimation of confidence measures. We validate the two proposed approaches through automatic speech recognition and dysarthric speech intelligibility assessment studies.Finally, in the context of privacy preservation, we develop a signal processing-based speech pseudonymization approach that alters voice source information and vocal tract system information based on prior knowledge to obfuscate the speaker identity, while retaining intelligibility, i.e. the phones and words remain recognizable. We validate the proposed pseudonymization approach through listening experiments and automatic evaluations.

EPFL2021

DNN-based Speech Synthesis: Importance of input features and training data

Philip Neil Garner

Deep neural networks (DNNs) have been recently introduced in speech synthesis. In this paper, an investigation on the importance of input features and training data on speaker dependent (SD) DNN-based speech synthesis is presented. Various aspects of the training procedure of DNNs are investigated in this work. Additionally, several training sets of different size (i.e., 13.5, 3.6 and 1.5 h of speech) are evaluated.

Springer Berlin Heidelberg2015

Word Sequence Modeling using Deep Learning

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state-of-the-art results in various fields such as computer vision, speech processing and natural language processing. The central idea behind these approaches is to learn features and models simultaneously, in an end-to-end manner, and making as few assumptions as possible. In NLP, word embeddings, mapping words in a dictionary on a continuous low-dimensional vector space, have proven to be very efficient for a large variety of tasks while requiring almost no a-priori linguistic assumptions. In this thesis, we investigate continuous representations of segments in a sentence for the purpose of solving NLP tasks that involve complex sentence-level relationships. Our sequence modelling approach is based on neural networks and takes advantage of word embeddings. A first approach models words in context in the form of continuous vector representations which are used to solve the task of interest. With the use of a compositional procedure, allowing arbitrarily-sized segments to be compressed onto continuous vectors, the model is able to consider long-range word dependencies as well. We first validate our approach on the task of bilingual word alignment, consisting in finding word correspondences between a sentence in two different languages. Source and target words in context are modeled using convolutional neural networks, obtaining representations that are later used to compute alignment scores. An aggregation operation enables unsupervised training for this task. We show that our model outperforms a standard generative model. The model above is extended to tackle phrase prediction tasks where phrases rather than single words are to be tagged. These tasks have been typically cast as classic word tagging problems using special tagging schemes to identify the segments boundaries. The proposed neural model focuses on learning fixed-size representations of arbitrarily-sized chunks of words that are used to solve the tagging task. A compositional operation is introduced in this work for the purpose of computing these representations. We demonstrate the viability of the proposed representations by evaluating the approach on the multiwork expression tagging task. The remainder of this thesis addresses the task of syntactic constituency parsing which, as opposed to the above tasks, aims at producing a structured output, in the form of a tree, of an input sentence. Syntactic parsing is cast as multiple phrase prediction problems that are solved recursively in a greedy manner. An extension using recursive compositional vector representations, allowing for lexical infor- mation to be propagated from early stages, is explored as well. This approach is evaluated on a standard corpus obtaining performance comparable to generative models with much shorter computation time. Finally, morphological tags are included as additional features, using a similar composition procedure, to improve the parsing performance for morphologically rich languages. State-of-the-art results were obtained for these task and languages.