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Showing 1 to 20 of 147 for “"Automatic speech recognition"”.
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Automatic Speech Recognition Quality Estimation
Evaluation of automatic speech recognition (ASR) systems is difficult and costly, since it requires manual transcriptions. This evaluation is usually done by computing word error rate (WER) that is the most popular metric in ASR community. Such computation is doable only if the manual references …
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Pronunciation learning for automatic speech recognition
… the lexicon remains the Achilles heel of modern automatic speech recognizers (ASRs). Unlike stochastic acoustic and language models that learn the values of their parameters from training data, the baseform pronunciations of words in an ASR vocabulary are typically specified manually, and do not …
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Speaker model adaptation in automatic speech recognition.
One of the main obstacles of automatic speech recognition is to achieve speaker independence. It is generally believed that the main difficulty is the inter-speaker variability in which the acoustic characteristics of different speakers are not the same. There are mainly three approaches to …
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Using graphone models in automatic speech recognition
… in several domains to enable detection and recognition of previously unknown words. For these experiments, graphones models are integrated into the SUMMIT speech recognition framework. First, graphones are applied to automatically generate pronunciations of restaurant names for a speech …
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A new structure for automatic speech recognition
Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
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Explainable artificial intelligence for inclusive automatic speech recognition
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01
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Dealing with linguistic mismatches for automatic speech recognition
Recent breakthroughs in automatic speech recognition (ASR) have resulted in a word error rate (WER) on par with human transcribers on the English Switchboard benchmark. However, dealing with linguistic mismatches between the training and testing data is still a significant challenge that remains …
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Feature-based pronunciation modeling for automatic speech recognition
Spoken language, especially conversational speech, is characterized by great variability in word pronunciation, including many variants that differ grossly from dictionary prototypes. This is one factor in the poor performance of automatic speech recognizers on conversational speech. One approach …
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Multilingual techniques for low resource automatic speech recognition
… world, there are only about 100 languages with Automatic Speech Recognition (ASR) capability. This is due to the fact that a vast amount of resources is required to build a speech recognizer. This often includes thousands of hours of transcribed speech data, a phonetic pronunciation dictionary …
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Multi-level acoustic modeling for automatic speech recognition
… modeling is commonly used in large-vocabulary Automatic Speech Recognition (ASR) systems as a way to model coarticulatory variations that occur during speech production. Typically, the local phoneme context is used as a means to define context-dependent units. Because the number of possible …
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Automatic Speech Recognition Using Deep Neural Networks: New Possibilities
Recently, automatic speech recognition (ASR) systems that use deep neural networks (DNNs) for acoustic modeling have attracted huge research interest. This is due to the recent results that have significantly raised the state of the art performance of ASR systems. This dissertation proposes a …
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Acoustic Model Adaptation For Reverberation Robust Automatic Speech Recognition
… provide the sensation of space. However, for automatic speech recognition even moderate amount of reverberation is very harmful. It corrupts the clean speech which leads to deterioration in the performance of the speech recognizer. Moreover, in the enclosed environment, reverberation has the …
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A comparative study of models for automatic speech recognition.
… a study of the most popular techniques for speech modelling at present, the Dynamic Programming approach, the Hidden Markov Model (HMM), and the Neural Network, which are also evaluated by experiments. The reason why the HMM outperforms the other techniques is examined rigorously in the …
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The Use of Formal Grammars in Automatic Speech Recognition
An automatic isolated-word recognition (IWR) system normally consists of a feature extractor (FH) followed by a recognition processor. Some form of 'training' is usually required in order to combat problems of variations in speech. This thesis presents the application of formal grammars to model a …
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The use of distinctive features for automatic speech recognition
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1991.
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Large-margin Gaussian mixture modeling for automatic speech recognition
… widely studied to improve the performance of automatic speech recognition systems. To enhance the generalization ability of discriminatively trained models, a large-margin training framework has recently been proposed. This work investigates large-margin training in detail, integrates the …
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A comparison of auditory models for automatic speech recognition
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.
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Evaluating the Effects of Automatic Speech Recognition Word Accuracy
Automatic Speech Recognition (ASR) research has been primarily focused towards large-scale systems and industry, while other areas that require attention are often over-looked by researchers. For this reason, this research looked at automatic speech recognition at the consumer level. Many …
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Using automatic speech recognition to evaluate Arabic to English transliteration
… systems. This thesis investigates whether or not speech recognition technology could be used to evaluate different Arabic-English transliteration systems. In order to do so there were 5 main objectives: firstly, to investigate the possibility of using English speech recognition engines to …
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