Articulatory features for speech-driven head motion synthesis

This study investigates the use of articulatory features for speech-driven head motion synthesis as opposed to prosody features such as F0 and energy that have been mainly used in the literature. In the proposed approach, multi-stream HMMs are trained jointly on the synchronous streams of speech and head motion data. Articulatory features can be regarded as an intermediate parametrisation of speech that are expected to have a close link with head movement. Measured head and articulatory movements acquired by EMA were synchronously recorded with speech. Measured articulatory data was compared to those predicted from speech using an HMM-based inversion mapping system trained in a semi-supervised fashion. Canonical correlation analysis (CCA) on a data set of free speech of 12 people shows that the articulatory features are more correlated with head rotation than prosodic and/or cepstral speech features. It is also shown that the synthesised head motion using articulatory features gave higher correlations with the original head motion than when only prosodic features are used. Index Terms: head motion synthesis, articulatory features, canonical correlation analysis, acoustic-to-articulatory mappin

Users' Perceptions of Environmental Control Systems

This paper presents users' perceptions of the benefits and challenges of environmental control systems, the data having been collected as part of a project developing a new speech-driven environmental control system. The first stage of this project collected data from existing users of speech-driven environmental control systems and provided information for the specification for the new device. A secondary analysis of this data revealed perceptions about environmental control systems in general and the results are presented here. Independence and control emerged as a key aspect of environmental control systems. In addition it was possible to identify other themes around topics such as perceptions of service delivery and provision. It can be easy for a non disabled person to overlook the importance of being able to independently change the television channel or make a phone call and this data reinforces the importance of this to people who use environmental control systems

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods

In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward-backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest formants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local spectral peaks shown by the model-driven LP-based methods. The refinement procedure can be plugged into the DeepFormants tracker with no need for any new data learning. Two refined DeepFormants trackers were compared with the original DeepFormants and with five known traditional trackers using the popular vocal tract resonance (VTR) corpus. The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis. In addition, by tracking formants using VTR speech that was corrupted by additive noise, the study showed that the refined DeepFormants trackers were more resilient to noise than the reference trackers. In general, these results suggest that LP-based model-driven approaches, which have traditionally been used in formant estimation, can be combined with a modern data-driven tracker easily with no further training to improve the tracker's performance.Comment: Computer Speech and Language, Vol. 81, Article 101515, June 202

Optimization of data-driven filterbank for automatic speaker verification

Most of the speech processing applications use triangular filters spaced in mel-scale for feature extraction. In this paper, we propose a new data-driven filter design method which optimizes filter parameters from a given speech data. First, we introduce a frame-selection based approach for developing speech-signal-based frequency warping scale. Then, we propose a new method for computing the filter frequency responses by using principal component analysis (PCA). The main advantage of the proposed method over the recently introduced deep learning based methods is that it requires very limited amount of unlabeled speech-data. We demonstrate that the proposed filterbank has more speaker discriminative power than commonly used mel filterbank as well as existing data-driven filterbank. We conduct automatic speaker verification (ASV) experiments with different corpora using various classifier back-ends. We show that the acoustic features created with proposed filterbank are better than existing mel-frequency cepstral coefficients (MFCCs) and speech-signal-based frequency cepstral coefficients (SFCCs) in most cases. In the experiments with VoxCeleb1 and popular i-vector back-end, we observe 9.75% relative improvement in equal error rate (EER) over MFCCs. Similarly, the relative improvement is 4.43% with recently introduced x-vector system. We obtain further improvement using fusion of the proposed method with standard MFCC-based approach.Comment: Published in Digital Signal Processing journal (Elsevier