15 accepted papers
Reverberation degrades signal quality and increases word error rates in automatic speech recognition (ASR). Reverberation suppression is, thus, a key component in listening enhancement devices and ASR front end. The weighted prediction error (WPE) is a prominent and effective method that gained popu…
One of the advantages of statistical parametric speech synthesis is the ability to alter some of the characteristics of the speech e.g. change the speaker, expression etc. In this paper we present a technique to adapt an expressive single speaker deep neural network (DNN) speech synthesis model to a…
In this paper, we explore the possibility of using the WaveNet architecture as a statistical vocoder. In that case, the generation of speech waveforms is locally conditioned only by acoustic features. Focusing on the single speaker case at the moment, we investigate the impact of the local condition…
Multi-domain dialogue systems face challenges such as scaling algorithms to handle large ontologies, or transferring trained policy models to unseen domains. We attempt to address these challenges by proposing a dialogue management architecture that has an abstracted view of the world but yet is abl…
Moderate and severe reverberation reduce speech intelligibility as a result of the overlap-masking effect, which constitutes the simultaneous observation of multiple delayed and attenuated copies of the speech signal. Recent progress has been made in ameliorating the degradation in intelligibility b…
This paper addresses the problem of speech emotion recognition from movie audio tracks. The recently collected Acted Facial Expression in the Wild 5.0 database is used. The aim is to discriminate among angry, happy, and neutral. We extract a relatively small number of features, a subset of which is…
In this paper, we present an expressive visual text to speech system (VTTS) based on a deep neural network (DNN). Given an input text sentence and a set of expression tags, the VTTS is able to produce not only the audio speech, but also the accompanying facial movements. The expressions can either b…
Statistical methods for Spoken Dialogue Systems have been shown to reduce the cost of development, while successfully handling a variety of applications. However, such systems are usually trained with simulated users or paid subjects in controlled settings. While this may be sufficient to jump-start…
In this paper, a recently proposed high-resolution Sinusoidal Model, dubbed the extended adaptive Quasi-Harmonic Model (eaQHM), is applied on modeling unvoiced speech sounds. Unvoiced speech sounds are parts of speech that are highly non-stationary in the time-frequency plane. Standard sinusoidal mo…
Although frequency analysis often leads us to a speech signal in the complex domain, the acoustic models we frequently use are designed for real-valued data. Phase is usually ignored or modelled separately from spectral amplitude. Here, we propose a complex-valued neural network (CVNN) for directly…
This paper presents a method to iteratively estimate phase information from speech in the cepstrum domain. It assumes that correct markings of pitch periods, which may not correspond to glottal closure instants (GCI), are available and can be used to extract the smooth spectral envelope of speech. B…
In statistical parametric speech synthesis such as Hidden Markov Model (HMM) based synthesis, one of the problems is in the over-smoothing of parameters, which leads to a muffled sensation in the synthesised output. In this paper, we propose an approach in which the high frequency spectrum is modell…
Significant improvements in intelligibility of speech in noise can be obtained by modifying the speech signal in the time and/or frequency domains. However, most speech intelligibility enhancement algorithms are designed to use clean speech as an input, and their performance suffers once the input s…
Sinusoidal vocoders can generate high quality speech, but they have not been extensively applied to statistical parametric speech synthesis. This paper presents two ways for using dynamic sinusoidal models for statistical speech synthesis, enabling the sinusoid parameters to be modelled in HMM-based…
In this paper we investigate the use of noise-robust features characterizing the speech excitation signal as complementary features to the usually considered vocal tract based features for Automatic Speech Recognition (ASR). The proposed Excitation-based Features (EBF) are tested in a state-of-the-a…