Abstract
This paper introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. In this variant of the HMM the observed signal is assumed to be Gaussian autoregressive and the probability density function is derived based on an approximation of the linear prediction error. A Baum-Welch style set of re-estimation formulas are then derived and used to infer the model parameters for a given data set, which correspond to linguistic structure in the context of speech data. The new set of re-estimation formulas are then applied to speech data and experimental results demonstrate inference of broad phonetic categories without prior knowledge of linguistic information. The experimental results and stability of this model are then briefly contrasted with historic experiments wherein phonetic information has been inferred directly from the speech signal using a similar autoregressive model.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 328-333 |
| Number of pages | 6 |
| Journal | Procedia Computer Science |
| Volume | 61 |
| DOIs | |
| State | Published - Jan 1 2015 |
| Event | Complex Adaptive Systems, 2015 - San Jose, United States Duration: Nov 2 2015 → Nov 4 2015 |
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Keywords
- autoregressive HMM
- hidden Markov model
- linear prediction
- speech signal processing
ASJC Scopus subject areas
- Computer Science(all)
Cite this
Autoregressive Hidden Markov Model and the Speech Signal. / Bryan, Jacob D.; Levinson, Stephen E.
In: Procedia Computer Science, Vol. 61, 01.01.2015, p. 328-333.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Autoregressive Hidden Markov Model and the Speech Signal
AU - Bryan, Jacob D.
AU - Levinson, Stephen E.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - This paper introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. In this variant of the HMM the observed signal is assumed to be Gaussian autoregressive and the probability density function is derived based on an approximation of the linear prediction error. A Baum-Welch style set of re-estimation formulas are then derived and used to infer the model parameters for a given data set, which correspond to linguistic structure in the context of speech data. The new set of re-estimation formulas are then applied to speech data and experimental results demonstrate inference of broad phonetic categories without prior knowledge of linguistic information. The experimental results and stability of this model are then briefly contrasted with historic experiments wherein phonetic information has been inferred directly from the speech signal using a similar autoregressive model.
AB - This paper introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. In this variant of the HMM the observed signal is assumed to be Gaussian autoregressive and the probability density function is derived based on an approximation of the linear prediction error. A Baum-Welch style set of re-estimation formulas are then derived and used to infer the model parameters for a given data set, which correspond to linguistic structure in the context of speech data. The new set of re-estimation formulas are then applied to speech data and experimental results demonstrate inference of broad phonetic categories without prior knowledge of linguistic information. The experimental results and stability of this model are then briefly contrasted with historic experiments wherein phonetic information has been inferred directly from the speech signal using a similar autoregressive model.
KW - autoregressive HMM
KW - hidden Markov model
KW - linear prediction
KW - speech signal processing
UR - http://www.scopus.com/inward/record.url?scp=84962681661&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962681661&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2015.09.151
DO - 10.1016/j.procs.2015.09.151
M3 - Conference article
AN - SCOPUS:84962681661
VL - 61
SP - 328
EP - 333
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
ER -