Name
Abstract | ||
|---|---|---|
While most speech enhancement algorithms improve speech quality, they may not improve speech intelligibility in noise. This paper focuses on the development of an algorithm that can be optimized for a specific acoustic environment and improve speech intelligibility. The proposed method decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is dominated by the target signal or the noise masker. Target-dominated T-F units are retained while masker-dominated T-F units are discarded. The Bayesian classifier is trained for each acoustic environment using an incremental approach that continuously updates the model parameters as more data become available. Listening experiments were conducted to assess the intelligibility of speech synthesized using the incrementally adapted models as a function of the number of training sentences. Results indicated substantial improvements in intelligibility (over 60% in babble at -5 dB SNR) with as few as ten training sentences in babble and at least 80 sentences in other noisy conditions. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TASL.2010.2041116 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
environment-optimized algorithms,time-frequency units,binary decisions,bayesian classifier,speech intelligibility,bayes methods,speech enhancement algorithm,target-dominated t-f unit,speech enhancement algorithms,acoustic noise,speech synthesis,improving speech intelligibility,t-f unit,input signal,environment optimized algorithms,environment-optimized algorithm,training sentence,speech quality,speech synthesizer,speech enhancement,masker-dominated t-f unit,acoustic environment,speech,feature extraction,time frequency,signal to noise ratio | Speech enhancement,Speech processing,Background noise,Computer science,Sound quality,Artificial intelligence,Intelligibility (communication),Noise,Speech synthesis,Pattern recognition,Signal-to-noise ratio,Algorithm,Speech recognition | Journal |
Volume | Issue | ISSN |
18 | 8 | 1558-7916 |
Citations | PageRank | References |
20 | 1.31 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gibak Kim | 1 | 103 | 7.38 |
| Philipos C. Loizou | 2 | 991 | 71.00 |