Publication

Phoneme Recognition Using Spectral Envelope and Modulation Frequency Features

2009
Reports

Abstract

We present a new feature extraction technique for phoneme recognition that uses short-term spectral envelope and modulation frequency features. These features are derived from sub-band temporal envelopes of speech estimated using Frequency Domain Linear Prediction (FDLP). While spectral envelope features are obtained by the short-term integration of the sub-band envelopes, the modulation frequency components are derived from the long-term evolution of the sub-band envelopes. These features are combined at the phoneme posterior level and used as features for a hybrid HMM-ANN phoneme recognizer. For the phoneme recognition task on the TIMIT database, the proposed features show an improvement of 4.7% over the other feature extraction techniques.

Official source

https://infoscience.epfl.ch/entities/publication/ea28ff78-21e7-435e-81a8-92a7dcf4856b

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2009
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/ea28ff78-21e7-435e-81a8-92a7dcf4856b

About this result

Related concepts (32)

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.

Frequency modulation

Frequency modulation (FM) is the encoding of information in a carrier wave by varying the instantaneous frequency of the wave. The technology is used in telecommunications, radio broadcasting, signal processing, and computing. In analog frequency modulation, such as radio broadcasting, of an audio signal representing voice or music, the instantaneous frequency deviation, i.e. the difference between the frequency of the carrier and its center frequency, has a functional relation to the modulating signal amplitude.

Feature (machine learning)

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.

Related publications (8)

Phoneme Recognition Using Spectral Envelope and Modulation Frequency Features

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