Waveform-based speaker representations for speech synthesis

Speaker adaptation is a key aspect of building a range of speech processing systems, for example personalised speech synthesis. For deep-learning based approaches, the model parameters are hard to interpret, making speaker adaptation more challenging. One widely used method to address this problem is to extract a fixed length vector as speaker representation, and use this as an additional input to the task-specific model. This allows speaker-specific output to be generated, without modifying the model parameters. However, the speaker representation is often extracted in a task-independent fashion. This allows the same approach to be used for a range of tasks, but the extracted representation is unlikely to be optimal for the specific task of interest. Furthermore, the features from which the speaker representation is extracted are usually pre-defined, often a standard speech representation. This may limit the available information that can be used. In this paper, an integrated optimisation framework for building a task specific speaker representation, making use of all the available information, is proposed. Speech synthesis is used as the example task. The speaker representation is derived from raw waveform, incorporating text information via an attention mechanism. This paper evaluates and compares this framework with standard task-independent forms.EPSRC International Doctoral Scholarship, reference number 10348827; St. John’s College Internal Graduate Scholarship; the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 655764; EPSRC grant EP/I031022/1 (Natural Speech Technology

On Using Backpropagation for Speech Texture Generation and Voice Conversion

Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthesis and voice conversion based on two mechanisms: approximate inversion of the representation learned by a speech recognition neural network, and on matching statistics of neuron activations between different source and target utterances. Similar to image texture synthesis and neural style transfer, the system works by optimizing a cost function with respect to the input waveform samples. To this end we use a differentiable mel-filterbank feature extraction pipeline and train a convolutional CTC speech recognition network. Our system is able to extract speaker characteristics from very limited amounts of target speaker data, as little as a few seconds, and can be used to generate realistic speech babble or reconstruct an utterance in a different voice.Comment: Accepted to ICASSP 201

Speech Processing in Computer Vision Applications

Deep learning has been recently proven to be a viable asset in determining features in the field of Speech Analysis. Deep learning methods like Convolutional Neural Networks facilitate the expansion of specific feature information in waveforms, allowing networks to create more feature dense representations of data. Our work attempts to address the problem of re-creating a face given a speaker\u27s voice and speaker identification using deep learning methods. In this work, we first review the fundamental background in speech processing and its related applications. Then we introduce novel deep learning-based methods to speech feature analysis. Finally, we will present our deep learning approaches to speaker identification and speech to face synthesis. The presented method can convert a speaker audio sample to an image of their predicted face. This framework is composed of several chained together networks, each with an essential step in the conversion process. These include Audio embedding, encoding, and face generation networks, respectively. Our experiments show that certain features can map to the face and that with a speaker\u27s voice, DNNs can create their face and that a GUI could be used in conjunction to display a speaker recognition network\u27s data