Large vocabulary continuous speech recognition with context-dependent DBN-HMMS

In this dissertation, a novel approach of integrating template matching with statistical modeling is proposed to improve continuous speech recognition. Hidden Markov Modeling (HMMs) has been the dominant approach in statistical speech recognition since it provides a principled way of jointly modeling speech spectral variations and time dynamics. However, HMMs have the shortcoming of assuming the observations being independent within each state, which makes it ineffective in modeling the details of speech temporal evolutions that are important for characterizing nonstationary speech sounds. Template-based methods make comparisons between a test pattern and the templates derived from training data, and therefore they are able to capture speech dynamics and time correlation of speech frames better than HMM based methods. However, template matching requires large memory space and computational time since feature vectors of training data need to be stored in computer memory for access at the recognition stage, which is difficult in large vocabulary continuous speech recognition (LVCSR). Our proposed approach takes advantages of both statistical modeling and template matching, which overcomes the weakness of conventional template-based method and is feasible for LVCSR. We use multiple Gaussian Mixture Model (GMM) indices to represent each frame of speech templates, and define the template unit to be context-dependent phone segments (triphone context). We also use phonetic decision trees borrowed from those commonly used in HMMs to tie triphone templates and predict triphones unseen in training data. Two local distances, log likelihood ratio (LLR) and Kullback-Leibler (KL) divergence, are proposed for dynamic time warping (DTW) based template matching. In order to reduce computational complexity and storage space, we propose methods of minimum distance template selection (MDTS) and maximum log-likelihood template selection (MLTS), and investigate a template compression method on top of template selection to further improve recognition performance. The template based methods were used to rescore lattices generated by baseline HMMs on the tasks of TIMIT continuous phone recognition and teleheath LVCSR and experimental results demonstrated that the proposed approach of integrating template matching with statistical modeling significantly improved recognition performances over the HMM baselines. The template selection methods also provided significant recognition accuracy improvements over the HMM baseline while largely reducing the computation and storage complexities. When all templates or MDTS were used, using the LLR local distance obtained better recognition performance than the KL divergence local distance. For MLTS and template compression, KL divergence local distance provided better performance than the LLR local distance, and the template compression method made further improvements over KL based MLTS. Since the templates were constructed based on the GMM indices extracted from HMM baselines, we also validated the effectiveness of the proposed template methods based on enhanced HMM baselines. Experimental results showed that LLR based all template method was able to consistently improve TIMIT phone recognition accuracies based on four enhanced HMM baselines. Prosodic features such as duration, energy, and pitch can reflect longer span information of speech than conventional single frame vectors but they have commonly been ignored by HMMs. Template based methods provide possibilities to conveniently integrate prosodic features into speech recognition, which has not been well studied in the past. In this dissertation, we investigate combining template based methods with the speech prosodic features of duration, energy and pitch to further improve speech recognition accuracy. The scores of prosodic information were computed by a GMM based method and a non-parametric method, and the prosodic scores were combined with the acoustic scores in triphone template matching. Experimental results obtained on the telehealth task showed that prosodic information had positive effects on vowel sound recognition.

This paper presents a deep neural network (DNN) based large vocabulary continuous speech recognition (LVCSR) system for Serbian, developed using the open-source Kaldi speech recognition toolkit. The DNNs are initialized using stacked restricted Boltzmann machines (RBMs) and trained using cross-entropy as the objective function and the standard error backpropagation procedure in order to provide posterior probability estimates for the hidden Markov model (HMM) states. Emission densities of HMM states are represented as Gaussian mixture models (GMMs). The recipes were modified based on the particularities of the Serbian language in order to achieve the optimal results. A corpus of approximately 90 hours of speech (21000 utterances) is used for the training. The performances are compared for two different sets of utterances between the baseline GMM-HMM algorithm and various DNN settings.KeywordsKaldi speech recognition toolkitContinuous speech recognitionDeep neural networksSerbian

—This paper presents the main improvements brought recently to the SpeeD automatic speech recognition system. Several aspects, such as speech and text resources acquisition, noise-robust speech features and feature transforms are discussed. All the updates in our ASR system are accompanied by experimental results illustrating significant improvements: between 30% and 35% relative WER reductions for various case studies (read/spontaneous speech, noisy/clean speech). In the last part of the paper, our ASR system is also compared with Google’s ASR system and a brief analysis of the results is presented. Keywords -ASR; LVCSR; noise-robust speech recognition; PNCC; LDA. I. I NTRODUCTION Large vocabulary continuous speech recognition (LVCSR) is still an unsolved topic for many languages. The reasons for this are (i) there is a lack of acoustic and linguistic resources needed for development (it is the case of so-called under-resourced languages) and (ii) the scientific research community is not stimulated by any national or international evaluation campaigns (as opposed to languages such as English, French or Chinese). The Romanian language is affected by both the aforementioned problems. In this context, the development of speech and language resources for automatic speech recognition (ASR) is a critical issue that must be addressed to push forward the research in this direction and create LVCSR systems comparable to those available for other languages. This is one of the main goals of the Speech and Dialogue (SpeeD) research group

We like to conversate with other people using both sounds and visuals, as our perception of speech is bimodal. Essentially echoing the same speech structure, we manage to integrate the two modalities and often understand the message better than with the eyes closed. In this work we would like to learn more about the visual nature of speech, coined lip-reading, and to make use of it towards better automatic speech recognition systems. Recent developments in the Machine Learning area, together with the release of suitable audio-visual datasets aimed at large vocabulary continuous speech recognition, have led to a renewal of the lip-reading topic, and allow us to address the recurring question of how to better integrate visual and acoustic speech.

Japanese large-vocabulary continuous-speech recognition using a newspaper corpus and broadcast news

We propose a method of incorporating a non-probabilistic grammar into large vocabulary continuous speech recognition (LVCSR). Our basic assumption is that the utterances to be recognized are grammatical to a sufficient degree, which enables us to decrease the word error rate by favouring grammatical phrases. We use a parser and a handcrafted grammar to identify grammatical phrases in word lattices produced by a speech recognizer. This information is then used to rescore the word lattice. We measured the benefit of our method by extending an LVCSR baseline system (based on hidden Markov models and a 4-gram language model) with our rescoring component. We achieved a statistically significant reduction in word error rate compared to the baseline system

The development of Multilingual Large Vocabulary Continuous Speech Recognition systems involves issues as: Language Identification, Acoustic-Phonetic Decoding, Language Modelling or the development of appropriated Language Resources. The interest on Multilingual Systems arouses because there are three official languages in the Basque Country (Basque, Spanish, and French), and there is much linguistic interaction among them, even if Basque has very different roots than the other two languages. This paper describes the development of a Language Identification (LID) system oriented to robust Multilingual Speech Recognition for the Basque context. The work presents hybrid strategies for LID, based on the selection of system elements by Support Vector Machines and Multilayer Perceptron classifiers and stochastic methods for speech recognition tasks (Hidden Markov Models and n-grams).KeywordsAutomatic Speech RecognitionMultilingual SystemLarge Vocabulary Continuous Speech RecognitionUnder-resourced LanguagesCross-lingual strategies

This paper deals with the use of a stochastic context‐free grammar (SCFG) for large vocabulary continuous speech recognition; in particular, an SCFG with phrase‐level dependency rules is built. Unlike n‐gram models, the SCFG can describe not only local constraints but also global constraints pertaining to the sentence as a whole, thus making possible language models with great expressive power. However, the inside‐outside algorithm must be used for estimation of the SCFG parameters, which involves a great amount of calculation, proportional to the third power of the number of nonterminal symbols and of the input string length. Hence, due to problems in dealing with extensive text corpora, the SCFG has hardly been applied as a language model for very large vocabulary continuous speech recognition. The proposed phrase‐level dependency SCFG allows a significant reduction of the computational load. In experiments with the EDR corpus, the proposed method proved effective. In experiments with the Mainichi corpus, a large‐scale phrase‐level dependency SCFG was built for a very large vocabulary continuous speech recognition system. Speech recognition tests with a vocabulary of about 5000 words showed that the proposed method could not compare with the trigram model in performance; however, when it was used in combination with a trigram model, the error rate was reduced by 14% compared to the trigram model alone. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(13): 48–59, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1172

Minimum classification error (MCE) based discriminative methods have been extensively studied and successfully applied to automatic speech recognition, speaker recognition, and utterance verification. Our goal is to modify the embedded string model based MCE algorithm to handle the case of a large number of cross-syllable trip hones used in a large vocabulary recognition system. A selective strategy about MCE based discriminative training method, in particular for Mandarin speech syllable loop recognition, is introduced. We use a syllable loop recognition task to evaluate the performance of the acoustic model of an established large vocabulary continuous speech recognition system. The basic idea is that since decoding errors occur in parts of the models in whole decoded sentences, it is reasonable to adjust the parameters of the wrong models. As a result, a weighted MCE formulation is derived, which can provide more effective convergence and about a 10% error rate reduction for a large training set. Our experiments show that, although the performance of the recognition system is improved, some original correct recognition results are misrecognized after discriminative training, and a divide and conquer strategy is proposed as a solution. Thus, the acoustic feature space is divided into two or more sub-spaces according to the discriminative training procedure. By combining these two methods, we obtain over 14.5% error rate reduction in syllable loop recognition.

This paper describes a reading quality scoring system based on large vocabulary continuous speech recognition (LVCSR). Our previous scoring system was based on forced alignment. A disadvantage of forced alignment based system is it can hardly catch huge kinds of reading miscues, while LVCSR based system avoids this disadvantage. The most challenge was that the LVCSR recognition rate was low on our corpus. To improve the recognition rate, we optimized our LVCSR engine for passage scoring tasks by presenting a novel dynamic language model (LM) constructing algorithm. The optimized LVCSR's recognition rate on test speech data was 70.2%, while the recognition rate of the original LVCSR on the same database was 37.9%. Our scoring method was to align the text reference and the confusion network generated from the LVCSR decoding result. The LVCSR based system reduced the scoring error rate of the baseline system by 14.5% relatively.

An acoustic model based on hidden Markov models with deep neural networks (DNN-HMM) has recently been proposed and achieved high recognition accuracy. In this paper, we investigated an elastic spectral distortion method to artificially augment training samples to help DNN-HMMs acquire enough robustness even when there are a limited number of training samples. We investigated three distortion methods - vocal tract length distortion, speech rate distortion, and frequency-axis random distortion - and evaluated those methods with Japanese lecture recordings. In a large vocabulary continuous speech recognition task with only 10 hours of training samples, a DNN-HMM trained with the elastic spectral distortion method achieved a 10.1% relative word error reduction compared with a normally trained DNN-HMM.

This paper describes the development of state-of-the-art large vocabulary continuous speech recognition (LVCSR) system for the Malayalam language with an application for visually challenged. For an LVCSR, building a high accurate acoustic models and large-scale language models are the challenging task. Speech corpus for training the system is collected from 80 native speakers in room environment ensuring the speaker variance. Mel-frequency Cepstral Coefficients (MFCC) method is used as a front-end to extract acoustic features from the input signal. Acoustic model is built on 30 hours of speech data based on Hidden Markov Model (HMM). A hybrid model, integrating rule based and statistical method is used to handle pronunciation variations in the dictionary. The best configuration of the system achieved word accuracy of 75% in average. Accuracy of the system is further increased up to 80% in average, by implementing speaker adaptation technique. The developed system is integrated to OpenOffice Writer together with TTS for making it user friendly editor for visually challenged people.

Lattice segmentation and minimum Bayes risk discriminative training for large vocabulary continuous speech recognition

The Tigrinya language is agglutinative and has a large number of inflected and derived forms of words. Therefore a Tigrinya large vocabulary continuous speech recognition system often has a large number of different units and a high out-of-vocabulary (OOV) rate if a word is used as a recognition unit of a language model (LM) and lexicon. Therefore a morpheme-based approach has often been used and a morpheme is used as the recognition unit to reduce the high OOV rate. This paper presents an automatic speech recognition experiment conducted to see the effect of OOV words on the performance speech recognition system for Tigrinya. We tried to solve the OOV problem by using morphemes as lexicon and language model units. It has been found that the morpheme-based recognition system is better lexical and language modeling units than words. An absolute improvement (in word recognition accuracy) of 3.45 token and 8.36 types has been obtained as a result of using a morph-based vocabulary.

Subspace Gaussian mixture based language modeling for large vocabulary continuous speech recognition