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On Modeling the Synergy Between Acoustic and Lexical Information for Pronunciation Lexicon Development

Related publications (41)

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Acoustic and Lexical Resource Constrained ASR using Language-Independent Acoustic Model and Language-Dependent Probabilistic Lexical Model

Ramya Rasipuram

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 ...

Idiap2014

Probabilistic Lexical Modeling and Grapheme-based Automatic Speech Recognition

Ramya Rasipuram

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 ...

Idiap2013

Probabilistic Lexical Modeling and Unsupervised Training for Zero-Resourced ASR

Ramya Rasipuram, Marzieh Razavi

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 ...

2013

Standard automatic speech recognition (ASR) systems use phonemes as subword units. Thus, one of the primary resource required to build a good ASR system is a well developed phoneme pronunciation lexicon. However, under-resourced languages typically lack su ...

2013

Unified Framework of Feature Based Adaptation for Statistical Speech Synthesis and Recognition

Lakshmi Babu Saheer

The advent of statistical parametric speech synthesis has paved new ways to a unified framework for hidden Markov model (HMM) based text to speech synthesis (TTS) and automatic speech recognition (ASR). The techniques and advancements made in the field of ...

EPFL2013

Improving Grapheme-based ASR by Probabilistic Lexical Modeling Approach

Ramya Rasipuram

There is growing interest in using graphemes as subword units, especially in the context of the rapid development of hidden Markov model (HMM) based automatic speech recognition (ASR) system, as it eliminates the need to build a phoneme pronunciation lexic ...

2013

Improving Grapheme-based ASR by Probabilistic Lexical Modeling Approach

Ramya Rasipuram

Idiap2013

Idiap2012

Ecole Polytechnique Federale de Lausanne (EPFL)2012

In recent works, the use of phone class-conditional posterior probabilities (posterior features) directly as features provided successful results in template-based ASR systems. In this paper, motivated by the high quality of current text-to-speech systems ...

2012