Publication

Sequence Classification with Input-Output Hidden Markov Models

2004
Reports

Abstract

We present a training and testing method for Input-Output Hidden Markov Model that is particularly suited for classification of sequences in which class information accumulates over time. We discuss two such cases: the discrimination of mental tasks from sequences of EEG features, common in Brain Computer Interface research, and phoneme classification from sequences of acoustic features for speech recognition. The objective function is modified so that training focuses on the improvement of classification accuracy. For both tasks the algorithm performs significantly better than the alternative solution proposed in the literature, specifically designed for other types of sequences.

Official source

https://infoscience.epfl.ch/entities/publication/bd5a4152-3b8b-44f7-bba6-20a9de6dedac

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2004
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/bd5a4152-3b8b-44f7-bba6-20a9de6dedac

About this result

Related concepts (32)

Speech processing

Speech processing is the study of speech signals and the processing methods of signals. The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing, applied to speech signals. Aspects of speech processing includes the acquisition, manipulation, storage, transfer and output of speech signals. Different speech processing tasks include speech recognition, speech synthesis, speaker diarization, speech enhancement, speaker recognition, etc.

Statistical classification

In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.

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.

Related publications (18)

Sequence Classification with Input-Output Hidden Markov Models

Chat with Graph Search

EEG-based self-paced decoding of upper limb movement intention in healthy subjects

Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks

A Kernel Wrapper for Phoneme Sequence Recognition