Data Mining Practical Machine Learning Tools and Techniques

Digital processing of speech signals

http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2011/Schuller11-RRE.pdf

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

A review on machine learning principles for multi-view biological data integration.

https://link.springer.com/content/pdf/10.1057%2Fjors.1990.171.pdf

Computer Intensive Methods for Testing Hypotheses: An Introduction

Feature extraction from smartphone inertial signals for human activity segmentation

This paper explores the potential universality of neural vocoders. We train a WaveRNN-based vocoder on 74 speakers coming from 17 languages. This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain scenario when the recording conditions are studio-quality. When the recordings show significant changes in quality, or when moving towards non-speech vocalizations or singing, the vocoder still significantly outperforms speaker-dependent vocoders, but operates at a lower average relative MUSHRA of 75%. These results are shown to be consistent across languages, regardless of them being seen during training (e.g. English or Japanese) or unseen (e.g. Wolof, Swahili, Ahmaric).

/pdf/towards-achieving-robust-universal-neural-vocoding-4k1k6la0js.pdf

Towards achieving robust universal neural vocoding

http://www.geintra-uah.org/system/files/2010-FinalPaperBarraSpecom.pdf

Analysis of statistical parametric and unit selection speech synthesis systems applied to emotional speech

We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained TTS model to mimic a new speaker using a small corpus of target utterances. We demonstrate that there does not exist a one-size-fits-all adaptation strategy, with convincing synthesis requiring a corpus-specific configuration of the hyper-parameters that control fine-tuning. By using Bayesian optimization to efficiently optimize these hyper-parameter values for a target speaker, we are able to perform adaptation with an average 30% improvement in speaker similarity over standard techniques. Results indicate, across multiple corpora, that BOFFIN TTS can learn to synthesize new speakers using less than ten minutes of audio, achieving the same naturalness as produced for the speakers used to train the base model.

/pdf/boffin-tts-few-shot-speaker-adaptation-by-bayesian-1oadwp6s9c.pdf

Roberto Barra-Chicote

Papers

Feature extraction from smartphone inertial signals for human activity segmentation

Towards achieving robust universal neural vocoding

Analysis of statistical parametric and unit selection speech synthesis systems applied to emotional speech

BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization

Towards achieving robust universal neural vocoding