Data Mining Practical Machine Learning Tools and Techniques

This paper introduces WaveNet, a deep neural network for generating raw audio
waveforms. The model is fully probabilistic and autoregressive, with the
predictive distribution for each audio sample conditioned on all previous ones;
nonetheless we show that it can be efficiently trained on data with tens of
thousands of samples per second of audio. When applied to text-to-speech, it
yields state-of-the-art performance, with human listeners rating it as
significantly more natural sounding than the best parametric and concatenative
systems for both English and Mandarin. A single WaveNet can capture the
characteristics of many different speakers with equal fidelity, and can switch
between them by conditioning on the speaker identity. When trained to model
music, we find that it generates novel and often highly realistic musical
fragments. We also show that it can be employed as a discriminative model,
returning promising results for phoneme recognition.

WaveNet: A Generative Model for Raw Audio

Recent advances in convolutional neural networks

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

Recent Advances in Convolutional Neural Networks

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL.

A Survey on Multi-Task Learning

The quality of text-to-speech (TTS) voices built from noisy speech is compromised. Enhancing the speech data before training has been shown to improve quality but voices built with clean speech are still preferred. In this paper we investigate two different approaches for speech enhancement to train TTS systems. In both approaches we train a recursive neural network (RNN) to map acoustic features extracted from noisy speech to features describing clean speech. The enhanced data is then used to train the TTS acoustic model. In one approach we use the features conventionally employed to train TTS acoustic models, i.e Mel cepstral (MCEP) coefficients, aperiodicity values and fundamental frequency (F0). In the other approach, following conventional speech enhancement methods, we train an RNN using only the MCEP coefficients extracted from the magnitude spectrum. The enhanced MCEP features and the phase extracted from noisy speech are combined to reconstruct the waveform which is then used to extract acoustic features to train the TTS system. We show that the second approach results in larger MCEP distortion but smaller F0 errors. Subjective evaluation shows that synthetic voices trained with data enhanced with this method were rated higher and with similar to scores to voices trained with clean speech.

/pdf/investigating-rnn-based-speech-enhancement-methods-for-noise-30jcimxkyp.pdf

Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech

Deep neural networks (DNNs) use a cascade of hidden representations to enable the learning of complex mappings from input to output features. They are able to learn the complex mapping from text-based linguistic features to speech acoustic features, and so perform text-to-speech synthesis. Recent results suggest that DNNs can produce more natural synthetic speech than conventional HMM-based statistical parametric systems. In this paper, we show that the hidden representation used within a DNN can be improved through the use of Multi-Task Learning, and that stacking multiple frames of hidden layer activations (stacked bottleneck features) also leads to improvements. Experimental results confirmed the effectiveness of the proposed methods, and in listening tests we find that stacked bottleneck features in particular offer a significant improvement over both a baseline DNN and a benchmark HMM system.

/pdf/deep-neural-networks-employing-multi-task-learning-and-43w7uxua4h.pdf

Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis

Noisy speech database for training speech enhancement algorithms and TTS models

https://www.cstr.ed.ac.uk/downloads/publications/2012/Cooke_SPCOM13.pdf

Evaluating the intelligibility benefit of speech modifications in known noise conditions

Quality of text-to-speech voices built from noisy recordings is diminished. In order to improve it we propose the use of a recurrent neural network to enhance acoustic parameters prior to training. We trained a deep recurrent neural network using a parallel database of noisy and clean acoustics parameters as input and output of the network. The database consisted of multiple speakers and diverse noise conditions. We investigated using text-derived features as an additional input of the network. We processed a noisy database of two other speakers using this network and used its output to train an HMM acoustic text-to-synthesis model for each voice. Listening experiment results showed that the voice built with enhanced parameters was ranked significantly higher than the ones trained with noisy speech and speech that has been enhanced using a conventional enhancement system. The text-derived features improved results only for the female voice, where it was ranked as highly as a voice trained with clean speech.

/pdf/speech-enhancement-for-a-noise-robust-text-to-speech-1yds05c8oi.pdf

Cassia Valentini-Botinhao

Papers

Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech

Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis

Noisy speech database for training speech enhancement algorithms and TTS models

Evaluating the intelligibility benefit of speech modifications in known noise conditions

Speech Enhancement for a Noise-Robust Text-to-Speech Synthesis System using Deep Recurrent Neural Networks