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Acoustic data-driven grapheme-to-phoneme conversion in the probabilistic lexical modeling framework

Publications associées (33)

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Current HMM-based low bit rate speech coding systems work with phonetic vocoders. Pitch contour coding (on frame or phoneme level) is usually fairly orthogonal to other speech coding parameters. We make an assumption in our work that the speech signal cont ...

Idiap2013

Acoustic Data-driven Grapheme-to-Phoneme Conversion using KL-HMM

Ramya Rasipuram

This paper proposes a novel grapheme-to-phoneme (G2P) conversion approach where first the probabilistic relation between graphemes and phonemes is captured from acoustic data using Kullback-Leibler divergence based hidden Markov model (KL-HMM) system. Then ...

2012

Combining Acoustic Data Driven G2P and Letter-to-Sound Rules for Under Resource Lexicon Generation

Ramya Rasipuram

In a recent work, we proposed an acoustic data-driven grapheme-to-phoneme (G2P) conversion approach, where the probabilistic relationship between graphemes and phonemes learned through acoustic data is used along with the orthographic transcription of word ...

2012

Acoustic Data-driven Grapheme-to-Phoneme Conversion using KL-HMM

Ramya Rasipuram

Idiap2011

Grapheme-based Automatic Speech Recognition using KL-HMM

Hervé Bourlard, Ramya Rasipuram, Guillermo Aradilla

The state-of-the-art automatic speech recognition (ASR) systems typically use phonemes as subword units. In this work, we present a novel grapheme-based ASR system that jointly models phoneme and grapheme information using Kullback-Leibler divergence-based ...

2011

On Joint Modelling of Grapheme and Phoneme Information using KL-HMM for ASR

Hervé Bourlard, Guillermo Aradilla

In this paper, we propose a simple approach to jointly model both grapheme and phoneme information using Kullback-Leibler divergence based HMM (KL-HMM) system. More specifically, graphemes are used as subword units and phoneme posterior probabilities estim ...

Idiap2009

A Large Margin Algorithm for Forced Alignment

We describe and analyze a discriminative algorithm for learning to align a phoneme sequence of a speech utterance with its acoustical signal counterpart by predicting a timing sequence representing the phoneme start times. In contrast to common HMM-based a ...

John Wiley and Sons2009

We investigate the detection of spoken terms in conversational speech using phoneme recognition with the objective of achieving smaller index size as well as faster search speed. Speech is processed and indexed as a sequence of one best phoneme sequence. W ...

2008

Fast Approximate Spoken Term Detection from Sequence of Phonemes

Hynek Hermansky, Joel Praveen Pinto

IDIAP2008

On Confusions in a Phoneme Recognizer

Hynek Hermansky, Joel Praveen Pinto

In this paper, we analyze the confusions patterns at three places in the hybrid phoneme recognition system. The confusions are analyzed at the pronunciation, the posterior probability, and the phoneme recognizer levels. The confusions show significant stru ...

2007