New speech recognition model: Hidden Conditional Neural Fields

Sep 25, 2013
Model architectures of HMM, HCFR and HCNF.

Toyohashi Tech researchers propose the Hidden Conditional Neural Fields (HCNF) model for continuous speech recognition. The model is a combination of the Hidden Conditional Random Fields (HCRF) and a Multi-Layer Perceptron (MLP), that is, an extension of Hidden Markov Model (HMM).

This new model has the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. Furthermore, the HCNF can incorporate many types of features from non-linear features can be extracted, and is trained by sequential criteria.

In this paper, the researchers describe the formulation of HCNF and examine three methods to further improve using HCNF, which was an objective function that explicitly considered training errors, provided a hierarchical tandem-style feature, and included a deep non-linear feature extractor for the observation function.

HCRF can use a deep feed forward (DNN) in the observation function, and therefore, a sophisticated pre-training algorithm such as the deep belief network (DBN) can be used to provide a deep observation function.

The research shows that HCNF can be trained realistically without any initial model and outperform the HCRF and triphone hidden Markov model trained by the minimum phone error (MPE) manner using for continuous English phoneme recognition on the TIMIT core test and Japanese phoneme recognition on the IPA 100 test set.

Explore further: Coping with floods—of water and data

More information: Fujii, Y., Yamamoto, K. and Nakagawa, S. Hidden Conditional Fields for Continuous Phoneme Speech Recognition, IEICE Trans. Inf.&Sys., E95-D,2094-2104 (2012). DOI: 10.1587/transinf.E95.D.2094

add to favorites email to friend print save as pdf

Related Stories

Speech recognition leaps forward

Aug 29, 2011

During Interspeech 2011, the 12th annual Conference of the International Speech Communication Association being held in Florence, Italy, from Aug. 28 to 31, researchers from Microsoft Research will present work that dramatically ...

Research aims to improve speech recognition software

Aug 11, 2010

Anyone who has used an automated airline reservation system has experienced the promise - and the frustration - inherent in today's automatic speech recognition technology. When it works, the computer "understands" that you ...

Bionic speech recognition

Sep 09, 2010

As speech recognition systems become more commonplace - on the computer desktop top, at the call centre and even in the car - it is increasingly important to ensure that the voice signal is as clear as possible before it ...

Smart listeners and smooth talkers

Nov 17, 2011

Human-like performance in speech technology could be just around the corner, thanks to a new research project that links three UK universities.

Recommended for you

Coping with floods—of water and data

Dec 19, 2014

Halloween 2013 brought real terror to an Austin, Texas, neighborhood, when a flash flood killed four residents and damaged roughly 1,200 homes. Following torrential rains, Onion Creek swept over its banks and inundated the ...

Cloud computing helps make sense of cloud forests

Dec 17, 2014

The forests that surround Campos do Jordao are among the foggiest places on Earth. With a canopy shrouded in mist much of time, these are the renowned cloud forests of the Brazilian state of São Paulo. It is here that researchers ...

User comments : 0

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.