Name
Abstract | ||
|---|---|---|
The auditory system, like the visual system, may be sensitive to abrupt stimulus changes, and the transient component in speech may be particularly critical to speech perception. If this component can be identified and selectively amplified, improved speech perception in background noise may be possible. This paper describes an algorithm to decompose speech into tonal, transient, and residual components. The modified discrete cosine transform (MDCT) was used to capture the tonal component and the wavelet transform was used to capture transient features. A hidden Markov chain (HMC) model and a hidden Markov tree (HMT) model were applied to capture statistical dependencies between the MDCT coefficients and between the wavelet coefficients, respectively. The transient component identified by the wavelet transform was selectively amplified and recombined with the original speech to generate modified speech, with energy adjusted to equal the energy of the original speech. The intelligibility of the original and modified speech was evaluated in eleven human subjects using the modified rhyme protocol. Word recognition rate results show that the modified speech can improve speech intelligibility at low SNR levels (8% at -15dB, 14% at -20dB, and 18% at -25dB) and has minimal effect on intelligibility at higher SNR levels. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.sigpro.2007.04.014 | Signal Processing |
Keywords | Field | DocType |
tonal component,improved speech perception,speech intelligibility,modified rhyme protocol,speech enhancement,original speech,speech perception,residual component,new signal decomposition method,transient component,modified discrete cosine,modified speech,confusion matrix,decomposition method,hidden markov chain,modified discrete cosine transform,wavelet transform,viterbi algorithm,word recognition,visual system,gaussian distribution | Speech enhancement,Speech processing,Pattern recognition,Modified discrete cosine transform,Markov model,Speech recognition,Artificial intelligence,Speech perception,Mathematics,Wavelet transform,Wavelet,Intelligibility (communication) | Journal |
Volume | Issue | ISSN |
87 | 11 | Signal Processing |
Citations | PageRank | References |
10 | 0.83 | 9 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Charturong Tantibundhit | 1 | 21 | 8.14 |
| J. R. Boston | 2 | 10 | 0.83 |
| Ching-chung Li | 3 | 383 | 65.47 |
| J. D. Durrant | 4 | 10 | 0.83 |
| Susan Shaiman | 5 | 22 | 4.67 |
| K. Kovacyk | 6 | 10 | 0.83 |
| A. El-Jaroudi | 7 | 241 | 34.35 |