Acoustic and Lexical Resource Constrained ASR using Language-Independent Acoustic Model and Language-Dependent Probabilistic Lexical Model
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Automatic speech recognition (ASR) systems incorporate expert knowledge of language or the linguistic expertise through the use of phone pronunciation lexicon (or dictionary) where each word is associated with a sequence of phones. The creation of phone pr ...
Standard automatic speech recognition (ASR) systems use phoneme-based pronunciation lexicon prepared by linguistic experts. When the hand crafted pronunciations fail to cover the vocabulary of a new domain, a grapheme-to-phoneme (G2P) converter is used to ...
Standard automatic speech recognition (ASR) systems rely on transcribed speech, language models, and pronunciation dictionaries to achieve state-of-the-art performance. The unavailability of these resources constrains the ASR technology to be available for ...
Phonological studies suggest that the typical subword units such as phones or phonemes used in automatic speech recognition systems can be decomposed into a set of features based on the articulators used to produce the sound. Most of the current approaches ...
Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic model- or feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully ...
One of the key challenge involved in building a statistical automatic speech recognition (ASR) system is modeling the relationship between lexical units (that are based on subword units in the pronunciation lexicon) and acoustic feature observations. To mo ...
Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems use phonemes as subword units. Thus, development of ASR system for a new language or domain depends upon the availability of a phoneme lexicon in the target language. In th ...
Automatic evaluation of non-native speech accentedness has potential implications for not only language learning and accent identification systems but also for speaker and speech recognition systems. From the perspective of speech production, the two prima ...
Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic model- or feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully ...
Introduction and purpose: Language is the medium by which people interact with all aspects of their worlds, whether economics, health, the environment, or technology. In both development programs and technology, however, language is usually given secondary ...