A hybrid approach to electrolaryngeal speech enhancement based on spectral subtraction and statistical voice conversion

We present a hybrid approach to improving naturalness of electrolaryngeal (EL) speech while minimizing degradation in intelligibility. An electrolarynx is a device that artificially generates excitation sounds to enable laryngectomees to produce EL speech. Although proficient laryngectomees can produce quite intelligible EL speech, it sounds very unnatural due to the mechanical excitation produced by the device. Moreover, the excitation sounds produced by the device often leak outside, adding noise to EL speech. To address these issues, previous work has proposed methods for EL speech enhancement through either noise reduction or voice conversion. The former usually causes no degradation in intelligibility but yields only small improvements in naturalness as the mechanical excitation sounds remain essentially unchanged. On the other hand, the latter method significantly improves naturalness of EL speech using spectral and excitation parameters of natural voices converted from acoustic parameters of EL speech, but it usually causes degradation in intelligibility owing to errors in conversion. We propose a hybrid method using the noise reduction method for enhancing spectral parameters and voice conversion method for predicting excitation parameters. The experimental results demonstrate the proposed method yields significant improvements in naturalness compared with EL speech while keeping intelligibility high enough.