This work proposes a method of model-based speech enhancement that uses a network of HMMs to first decode noisy speech and to then synthesise a set of features that enables a speech production model to reconstruct clean speech. The motivation is to remove the distortion and residual and musical noises that are associated with conventional filteringbased methods of speech enhancement. STRAIGHT forms the speech production model for speech reconstruction and requires a time-frequency spectral surface, aperiodicity and a fundamental frequency contour. The technique of HMM-based synthesis is used to create the estimate of the timefrequency surface, and aperiodicity after the model and state sequence is obtained from HMM decoding of the input noisy speech. Fundamental frequency were found to be best estimated using the PEFAC method rather than synthesis from the HMMs. For the robust HMM decoding in noisy conditions it is necessary for the HMMs to model noisy speech and consequently noise adaptation is investigated to achieve this and its resulting effect on the reconstructed speech measured. Even with such noise adaptation to match the HMMs to the noisy conditions, decoding errors arise, both in terms of incorrect decoding and time alignment errors. Confidence measures are developed to identify such errors and then compensation methods developed to conceal these errors in the enhanced speech signal. Speech quality and intelligibility analysis is first applied in terms of PESQ and NCM showing the superiority of the proposed method against conventional methods at low SNRs. Three way subjective MOS listening test then discovers the performance of the proposed method overwhelmingly surpass the conventional methods over all noise conditions and then a subjective word recognition test shows an advantage of the proposed method over speech intelligibility to the conventional methods at low SNRs.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:716442 |
| Date | January 2017 |
| Creators | Kato, Akihiro |
| Publisher | University of East Anglia |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ueaeprints.uea.ac.uk/63950/ |
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