Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM)

This paper presents procedures used for development of a Croatian large vocabulary automatic speech recognition system (LVASR). The proposed acoustic model is based on context-dependent triphone hidden Markov models and Croatian phonetic rules. Different acoustic and language models, developed using a large collection of Croatian speech, are discussed and compared. The paper proposes the best feature vectors and acoustic modeling procedures using which lowest word error rates for Croatian speech are achieved. In addition, Croatian language modeling procedures are evaluated and adopted for speaker independent spontaneous speech recognition. Presented experiments and results show that the proposed approach for automatic speech recognition using context-dependent acoustic modeling based on Croatian phonetic rules and a parameter tying procedure can be used for efficient Croatian large vocabulary speech recognition with word error rates below 5%.

Phonemes and allophones are the basic speech units for acoustic modeling in the majority of contemporary HMM based speech recognizers. Grapheme-based acoustic sub-word units were applied to multi-lingual and cross-lingual acoustic modeling in many tasks. Grapheme and phoneme based mono-, cross- and bilingual speech recognition of Czech and Slovak in the small and medium vocabulary task has been studied in our previous work. In this article we compare grapheme and phoneme based approach to acoustic modeling and model unit selection in large vocabulary continuous speech recognition (LVCSR) task in Slovak. The main goal of our experimental work is to investigate a possibility to select an optimal set of sub-word units for Slovak LVCSR system.

We report on some recent improvements to an HMM-based, continuous speech recognition system which is being developed at AT&T Bell Laboratories. These advances, which include the incorporation of inter-word, context-dependent units and an improved feature analysis, lead to a recognition system which achieve better than 95% word accuracy for speaker independent recognition of the 1000-word, DARPA resource management task using the standard word-pair grammar (with a perplexity of about 60). It will be shown that the incorporation of inter-word units into training results in better acoustic models of word juncture coarticulation and gives a 20% reduction in error rate. The effect of an improved set of spectral and log energy features is to further reduce word error rate by about 30%. We also found that the spectral vectors, corresponding to the same speech unit, behave differently statistically, depending on whether they are at word boundaries or within a word. The results suggest that intra-word and inter-word units should be modeled independently, even when they appear in the same context. Using a set of sub-word units which included variants for intra-word and inter-word, context-dependent phones, an additional decrease of about 10% in word error rate resulted.

Widespread use of large‐vocabulary continuous speech recognition systems has recently occurred, encouraging the application of speech recognition techniques to various problems. One of the factors that adversely affect the performance of speech recognition systems is a mismatch between the acoustic properties of the speech of the system user and the acoustic model. The speech of young or middle‐aged adults is generally used in constructing the acoustic model. Thus, a mismatch occurs between the model and the acoustic properties of the speech of the elderly, which may degrade the recognition rate. In this study, a large‐scale elderly speech database (200 sentences ×301 subjects) is used to train the acoustic model, and the resulting elderly acoustic model is evaluated by using a large‐vocabulary continuous speech recognition system. In the experiments, the word recognition rate was improved by 3 to 5% compared to the recognition results of an acoustic model trained by young or middle‐aged adult speech, namely, by the JNAS speech database (150 sentences ×260 subjects, average 28.6 years). It is also verified experimentally that the recognition rate is further improved in speaker adaptation to elderly speech by making use of an acoustic model trained by elderly speech. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 87(7): 49–57, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20101

A large-vocabulary continuous-speech recognition (LVCSR) system was developed and evaluated. To evaluate the system, a Japanese business-newspaper speech corpus was designed and recorded. The corpus was designed so that is can be used for Japanese LVCSR research in the same way that the Wall Street Journal (WSJ) corpus, for example, is used for English LVCSR research. Since Japanese sentences are written without spaces between words, a morphological analysis was introduced to segment sentences into words so that word n-gram language models could be used. To enable the use of detailed word n-gram (n/spl ges/3) language models, a two-pass decoding strategy was applied. Context-dependent (CD) phone models and word trigram language models reduced the word error rate from 80.2% to 10.1% (an error reduction of about 88%). This result shows that CD phoneme modeling and word trigram language models can be used effectively in Japanese LVCSR.

This paper introduces a novel method for the training of a complementary acoustic model with respect to set of given acoustic models. The method is based upon an extension of the Minimum Phone Error (MPE) criterion and aims at producing a model that makes complementary phone errors to those already trained. The technique is therefore called Complementary Phone Error (CPE) training. The method is evaluated using an Arabic large vocabulary continuous speech recognition task. Reductions in word error rate (WER) after combination with a CPE-trained system were obtained with up to 0.7% absolute for a system trained on 172 hours of acoustic data and up to 0.2% absolute for the final system trained on nearly 2000 hours of Arabic data.

In this paper, we present a novel method to regulate lexical connections among morpheme-based pronunciation lexicons for Korean large vocabulary continuous speech recognition (LVCSR) systems. A pronunciation dictionary plays an important role in subword-based LVCSR in that pronunciation variations such as coarticulation will deteriorate the performance of an LVCSR system if it is not well accounted for. In general, pronunciation variations are modeled by applying phonological variations with all possible phonemic contexts. In order to achieve high recognition performance, current speech recognition systems impose constraints among lexicons using both morphological and phonetic knowledge. This paper suggests a method both to refine pronunciation variations according to cross-morpheme phonetic information and to regulate the connections between pronunciation variants. This method effectively excludes improper connections between pronunciation lexicons, and thus the proposed method gave a 27% reduction in word error rate over the recognizer with conventional lexicons relatively.

A fast and memory-efficient N-gram language model lookup method for large vocabulary continuous speech recognition

An improved two-stage mixed language model approach for handling out-of-vocabulary words in large vocabulary continuous speech recognition

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

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

Current hidden Markov acoustic modeling for large vocabulary continuous speech recognition (LVCSR) relies on the availability of abundant labeled transcriptions. Given that speech labeling is both expensive and time-consuming while there is a huge amount of unlabeled data easily available nowadays, semi-supervised learning (SSL) from both labeled and unlabeled data which aims to reduce the development cost for LVCSR becomes more important than ever. In this paper, we propose SSL for LVCSR by using the multiple views learned from different acoustic features and randomized decision trees. In addition, we develop the multi-objective learning of HMM-based acoustic models by optimizing a hybrid criterion which is established by the combination of the discriminative mutual information from labeled data and the entropy from unlabeled data. Experiments conducted on Broadcast News show the benefits of proposed methods.

This work extends and improves a recently introduced (Dec. 2007) dynamic Bayesian network (DBN) based audio-visual automatic speech recognition (AV-ASR) system. That system models the audio and visual components of speech as being composed of the same sub-word units when, in fact, this is not psycholinguistically true. We extend the system to model the audio and visual streams as being composed of separate, yet related, sub-word units. We also introduce a novel stream weighting structure incorporated into the model itself. In doing so, our system makes improvements in word error rate (WER) and overall recognition accuracy in a large vocabulary continuous speech recognition task (LVCSR). The ldquobestrdquo performing proposed system attains a WER of 66.71%whereas the ldquobestrdquo baseline system performs at a WER of 64.30%. The proposed system also improves accuracy to 45.95% from 39.40%.

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

Multitask learning (MTL) is helpful for improving the performance of related tasks when the training dataset is limited and sparse, especially for low-resource languages. Amharic is a low-resource language and suffers from the problems of training data scarcity, sparsity, and unevenness. Consequently, fundamental acoustic units-based speech recognizers perform worse compared with the speech recognizers of technologically favored languages. This paper presents the results of our contributions to the use of various hybrid acoustic modeling units for the Amharic language. The fundamental acoustic units, namely, syllable, phone, and rounded phone units-based deep neural network (DNN) models have been developed. Various hybrid acoustic units have been investigated by jointly training the fundamental acoustic units via the MTL technique. Those hybrid units and the fundamental units are discussed and compared. The experimental results demonstrate that all the fundamental units-based DNN models outperform the Gaussian mixture models (GMM) with relative performance improvements of 14.14%-23.31%. All the hybrid units outperform the fundamental acoustic units with relative performance improvements of 1.33%-4.27%. The syllable and phone units exhibit higher performance under sufficient and limited training datasets, respectively. All the hybrid units are useful with both sufficient and limited training datasets and outperformed the fundamental units. Overall, our results show that DNN is an effective acoustic modeling technique for the Amharic language. The context-dependent (CD) syllable is the more suitable unit if a sufficient training corpus is available and the accuracy of the recognizer is prioritized. The CD phone is a superior unit if the available training dataset is limited and realizes the highest accuracy and fast recognition speed. The hybrid acoustic units perform the best under both sufficient and limited training datasets and achieve the highest accuracy.