Estimation of the glottal source from coded telephone speech using deep neural networks

Estimation of glottal source information can be performed non-invasively from speech by using glottal inverse filtering (GIF) methods. However, the existing GIF methods are sensitive even to slight distortions in speech signals under different realistic scenarios, for example, in coded telephone speech. Therefore, there is a need for robust GIF methods which could accurately estimate glottal flows from coded telephone speech. To address the issue of robust GIF, this paper proposes a new deep neural net-based glottal inverse filtering (DNN-GIF) method for estimation of glottal source from coded telephone speech. The proposed DNN-GIF method utilizes both coded and clean versions of speech signal during training. DNN is used to map the speech features extracted from coded speech with the glottal flows estimated from the corresponding clean speech. The glottal flows are estimated from the clean speech by using quasi closed phase analysis (QCP). To generate coded telephone speech, adaptive multi-rate (AMR) codec is utilized which operates in two transmission bandwidths: narrow band (300 Hz - 3.4 kHz) and wide band (50 Hz - 7 kHz). The glottal source parameters were computed from the proposed and existing GIF methods by using vowels obtained from natural speech data as well as from artificial speech production models. The errors in glottal source parameters indicate that the proposed DNN-GIF method has considerably improved the glottal flow estimation under coded condition for both low- and high-pitched vowels. The proposed DNN-GIF method can be utilized to accurately11In this article, the term “accurate/accuracy” is used only when referring to quantitative, objective measures. extract glottal source -based features from coded telephone speech which can be used to improve the performance of speech technology applications such as speaker recognition, emotion recognition and telemonitoring of neurodegerenerative diseases.