181 research outputs found
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
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
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
A Speech Recognizer based on Multiclass SVMs with HMM-Guided Segmentation
Automatic Speech Recognition (ASR) is essentially a problem of pattern
classification, however, the time dimension of the speech signal has
prevented to pose ASR as a simple static classification problem. Support
Vector Machine (SVM) classifiers could provide an appropriate solution,
since they are very well adapted to high-dimensional classification problems.
Nevertheless, the use of SVMs for ASR is by no means straightforward,
mainly because SVM classifiers require an input of fixed-dimension.
In this paper we study the use of a HMM-based segmentation as a mean to
get the fixed-dimension input vectors required by SVMs, in a problem of
isolated-digit recognition. Different configurations for all the parameters
involved have been tested. Also, we deal with the problem of multi-class
classification (as SVMs are initially binary classifers), studying two of the
most popular approaches: 1-vs-all and 1-vs-1
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
Master of Science
thesisPresently, speech recognition is gaining worldwide popularity in applications like Google Voice, speech-to-text reporter (speech-to-text transcription, video captioning, real-time transcriptions), hands-free computing, and video games. Research has been done for several years and many speech recognizers have been built. However, most of the speech recognizers fail to recognize the speech accurately. Consider the well-known application of Google Voice, which aids in users search of the web using voice. Though Google Voice does a good job in transcribing the spoken words, it does not accurately recognize the words spoken with different accents. With the fact that several accents are evolving around the world, it is essential to train the speech recognizer to recognize accented speech. Accent classification is defined as the problem of classifying the accents in a given language. This thesis explores various methods to identify the accents. We introduce a new concept of clustering windows of a speech signal and learn a distance metric using specific distance measure over phonetic strings to classify the accents. A language structure is incorporated to learn this distance metric. We also show how kernel approximation algorithms help in learning a distance metric
Phonologically-Informed Speech Coding for Automatic Speech Recognition-based Foreign Language Pronunciation Training
Automatic speech recognition (ASR) and computer-assisted pronunciation training (CAPT) systems used in foreign-language educational contexts are often not developed with the specific task of second-language acquisition in mind. Systems that are built for this task are often excessively targeted to one native language (L1) or a single phonemic contrast and are therefore burdensome to train. Current algorithms have been shown to provide erroneous feedback to learners and show inconsistencies between human and computer perception. These discrepancies have thus far hindered more extensive application of ASR in educational systems.
This thesis reviews the computational models of the human perception of American English vowels for use in an educational context; exploring and comparing two types of acoustic representation: a low-dimensionality linguistically-informed formant representation and more traditional Mel frequency cepstral coefficients (MFCCs). We first compare two algorithms for phoneme classification (support vector machines and long short-term memory recurrent neural networks) trained on American English vowel productions from the TIMIT corpus. We then conduct a perceptual study of non-native English vowel productions perceived by native American English speakers. We compare the results of the computational experiment and the human perception experiment to assess human/model agreement. Dissimilarities between human and model classification are explored. More phonologically-informed audio signal representations should create a more human-aligned, less L1-dependent vowel classification system with higher interpretability that can be further refined with more phonetic- and/or phonological-based research. Results show that linguistically-informed speech coding produces results that better align with human classification, supporting use of the proposed coding for ASR-based CAPT
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