# Whispered Speech Conversion Based on the Inversion of Mel Frequency Cepstral Coefficient Features
## 1 Introduction  
Whispered speech is a method of articulation different from normal speech [1]: it is produced without vibration of the vocal cords at a low sound level, which causes the voiced sound of whispered speech to have no fundamental frequency and an energy 20 dB less than that of normal speech [2]. Because of these characteristics, whispered speech is widely used in places where loud noises are prohibited such as conference rooms, libraries, and concert halls. Because of its potential applications [3; 4; 5; 6], in recent years, studies on whispered speech (e.g., whispered speech emotion recognition, whispered speech enhancement, whispered speech recognition, and whispered speech conversion) have gradually attracted researchers' attention. Among them, whispered speech conversion, a technique that converts whispered speech to normal speech, has been widely used in mobile communication, medical equipment, security monitoring, and crime identification [7; 8; 9]. For example, for a laryngeal cancer patient who has undergone a laryngectomy, the conversion of whispered speech to normal speech can greatly improve the patient's speech communication experience and efficiency [10]. At present, whispered speech conversion is mainly divided into two categories. One is rule-based whisper conversion, mainly using empirical observation or statistical modeling to generate transformation rules. The other one is machine learning based, which includes Gaussian mixture model and neural network method [11]. In terms of the conversion of whispered speech to normal speech, researchers used multiple excited linear prediction method to implement Chinese speech reconstruction [12; 13], in which normal speech is reconstructed by adding fundamental frequencies to the speech energy based on a whispered speech formant. However, the formant distribution of whispered speech is notidentical to that of normal speech, and its formant frequencies are shifted to high frequencies [14]; therefore, the sound quality of the reconstructed speech was poor. [PERSON] et al. adopted the mixed excitation linear prediction (MELP) model [15] to reconstruct whispered speech, where the whispered speech is divided into five frequency bands. Of these five frequency bands, four low-frequency bands were treated as voiced excitation, and the high-frequency band was treated as unvoiced excitation, to which fundamental frequencies are added based on the energy of speech, modifying the characteristics of the formant spectrum. The advantage of this method is its easy application to communication systems; however, because the added fundamental frequency is more monotonous than normal speech, the reconstructed speech often sounds \"metallic\". [PERSON] et al. improved the code-excited linear prediction (CELP) method by adding a whispered speech preprocessing module, a fundamental frequency-generation module and a formant modification module to achieve the conversion of whispered speech to normal speech. Their model has very limited effectiveness on continuous speech and is prone to generate quantization errors when performing vector quantization on an excitation source [16]. [PERSON] et al. proposed a whispered speech reconstruction system based on homomorphic signal processing and relative entropy segmentation [17; 18], which can better eliminate the metallic sound caused by adding fundamental frequencies, regardless of rhyme. The above methods are all rule-based whispered speech conversion techniques. In recent years, an increasing number of scholars have begun to use the statistical distribution characteristics of speech features to convert speeches and adopt probabilistic methods to achieve the prediction of target feature vectors from source feature vectors. Among them, the Gaussian mixture model (GMM) is the most widely used. [PERSON] et al. applied the GMM-based speech conversion methods to whispered speech conversion for the first time [19; 20; 21; 22; 23], making it possible to convert whispered speech to normal speech. In addition, [PERSON] et al.