SVMs for Automatic Speech Recognition: a Survey

Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact. During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed. These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research

Data Balancing for Efficient Training of Hybrid ANN/HMM Automatic Speech Recognition Systems

Hybrid speech recognizers, where the estimation of the emission pdf of the states of Hidden Markov Models (HMMs), usually carried out using Gaussian Mixture Models (GMMs), is substituted by Artificial Neural Networks (ANNs) have several advantages over the classical systems. However, to obtain performance improvements, the computational requirements are heavily increased because of the need to train the ANN. Departing from the observation of the remarkable skewness of speech data, this paper proposes sifting out the training set and balancing the amount of samples per class. With this method the training time has been reduced 18 times while obtaining performances similar to or even better than those with the whole database, especially in noisy environments. However, the application of these reduced sets is not straightforward. To avoid the mismatch between training and testing conditions created by the modification of the distribution of the training data, a proper scaling of the a posteriori probabilities obtained and a resizing of the context window need to be performed as demonstrated in the paper.This work was supported in part by the regional grant (Comunidad Autónoma de Madrid-UC3M) CCG06-UC3M/TIC-0812 and in part by a project funded by the Spanish Ministry of Science and Innovation (TEC 2008-06382).Publicad

Affective analysis of customer service calls

This paper presents an affective and acoustic-prosodic analysis of a call-center corpus (700 phone calls with corresponding customer satisfaction levels). Our main goal is to understand how customers’ satisfaction correlates to the acoustic-prosodic and affective information (emotions and personality traits) of the interactions. A subset of 30 calls was manually annotated with emotions (frustrated vs.neutral) and personality traits (Big-Five model). Results on automatic satisfaction prediction from acoustic-prosodic features show a number of very informative linguistic knowledge-based features, especially pitch and energy ranges. The affective analysis also provides encouraging results, relating low/high satisfaction levels with the presence/absence of customer frustration. Concerning personality, customers tend to express signs of anxiety and nervousness, while agents are generally perceived as extroverted and open.info:eu-repo/semantics/publishedVersio

A semi-supervised learning approach for acoustic-prosodic personality perception in under-resourced domains

Automatic personality analysis has gained attention in the last years as a fundamental dimension in human-To-human and human-To-machine interaction. However, it still suffers from limited number and size of speech corpora for specific domains, such as the assessment of children's personality. This paper investigates a semi-supervised training approach to tackle this scenario. We devise an experimental setup with age and language mismatch and two training sets: A small labeled training set from the Interspeech 2012 Personality Sub-challenge, containing French adult speech labeled with personality OCEAN traits, and a large unlabeled training set of Portuguese children's speech. As test set, a corpus of Portuguese children's speech labeled with OCEAN traits is used. Based on this setting, we investigate a weak supervision approach that iteratively refines an initial model trained with the labeled data-set using the unlabeled data-set. We also investigate knowledge-based features, which leverage expert knowledge in acoustic-prosodic cues and thus need no extra data. Results show that, despite the large mismatch imposed by language and age differences, it is possible to attain improvements with these techniques, pointing both to the benefits of using a weak supervision and expert-based acoustic-prosodic features across age and language

Real-time robust automatic speech recognition using compact support vector machines

In the last years, support vector machines (SVMs) have shown excellent performance in many applications, especially in the presence of noise. In particular, SVMs offer several advantages over artificial neural networks (ANNs) that have attracted the attention of the speech processing community. Nevertheless, their high computational requirements prevent them from being used in practice in automatic speech recognition (ASR), where ANNs have proven to be successful. The high complexity of SVMs in this context arises from the use of huge speech training databases with millions of samples and highly overlapped classes. This paper suggests the use of a weighted least squares (WLS) training procedure that facilitates the possibility of imposing a compact semiparametric model on the SVM, which results in a dramatic complexity reduction. Such a complexity reduction with respect to conventional SVMs, which is between two and three orders of magnitude, allows the proposed hybrid WLS-SVC/HMM system to perform real-time speech decoding on a connected-digit recognition task (SpeechDat Spanish database). The experimental evaluation of the proposed system shows encouraging performance levels in clean and noisy conditions, although further improvements are required to reach the maturity level of current context-dependent HMM based recognizers.Spanish Ministry of Science and Innovation TEC 2008-06382 and TEC 2008-02473 and Comunidad Autónoma de Madrid-UC3M CCG10-UC3M/TIC-5304.Publicad

Uma abordagem de aprendizagem semissupervisionada para a classificação automática de personalidade baseada em pistas acústico-prosódicas

Automatic personality analysis has gained great attention in the last years as a fundamental dimension in human-machine interactions. However, the development of this technology in some domains, such as the classification of children’s personality, has been hindered by the limited number and size of the available speech corpora due to ethical concerns on collecting such corpora. To circumvent the lack of data, we have investigated the application of a semi-supervised training approach that makes use of heterogeneous (age and language mismatches) and partially non-labelled data sets. Namely, preliminary personality models trained using a small labelled data set with French speaking adults are iteratively refined using a larger unlabeled set of Portuguese children’s speech, whereas a labelled corpus of Portuguese children is used for evaluation. We also investigated speech representations based on prior linguistic knowledge on acoustic-prosodic clues for personality classification tasks and have analysed their relevance in the assessment of each personality trait. The results point out to the potential of applying semi-supervised learning approaches with heterogeneous data sets to overcome the lack of labelled data in under-resourced domains, and to the existence of acousticprosodic clues shared by speakers with different languages and ages, which allows for the classification of personality independently of these variables.info:eu-repo/semantics/acceptedVersio

Robust ASR using Support Vector Machines

The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units. In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM–SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in Shimodaira et al. [Shimodaira, H., Noma, K., Nakai, M., Sagayama, S., 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In: Proc. Eurospeech, Aalborg, Denmark, pp. 1841–1844] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.Publicad