Publication

INTEGRATING ARTICULATORY FEATURES USING KULLBACK-LEIBLER DIVERGENCE BASED ACOUSTIC MODEL FOR PHONEME RECOGNITION

Mathew Magimai Doss
2011
Reports

Abstract

In this paper, we propose a novel framework to integrate articulatory features (AFs) into HMM- based ASR system. This is achieved by using posterior probabilities of different AFs (estimated by multilayer perceptrons) directly as observation features in Kullback-Leibler divergence based HMM (KL-HMM) system. On the TIMIT phoneme recognition task, the proposed framework yields a phoneme recognition accuracy of 72.4% which is comparable to KL-HMM system using posterior probabilities of phonemes as features (72.7%). Furthermore, a best performance of 73.5% phoneme recognition accuracy is achieved by jointly modelling AF probabilities and phoneme probabilities as features. This shows the efficacy and flexibility of the proposed approach.

Official source

https://infoscience.epfl.ch/entities/publication/acc63b7e-f38b-41ed-b2a4-97a8e8e33c2b

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Mathew Magimai Doss
2011
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/acc63b7e-f38b-41ed-b2a4-97a8e8e33c2b

About this result

Related concepts (22)

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Speech perception

Speech perception is the process by which the sounds of language are heard, interpreted, and understood. The study of speech perception is closely linked to the fields of phonology and phonetics in linguistics and cognitive psychology and perception in psychology. Research in speech perception seeks to understand how human listeners recognize speech sounds and use this information to understand spoken language.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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